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Sams Teach Yourself Microsoft

SQL

Server T-SQL in 10 Minutes

Second Edition

Ben Forta

800 East 96th Street, Indianapolis, Indiana 46240 USA

Sams Teach Yourself Microsoft

SQL Server T-SQL in 10 Minutes, Second

Edition

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any liability assumed for damages resulting from the use of the information

contained herein.

ISBN-13: 978-0-672-33792-5

ISBN-10: 0-672-33792-4

Printed in the United States of America

First Printing: December 2016

Library of Congress Control Number: 2016916352

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Contents
Introduction
Who Is This Book For?
Companion Website
Conventions Used in This Book
1 Understanding SQL
Database Basics
What Is SQL?
Try It Yourself
Summary
2 Introducing SQL Server
What Is SQL Server?
Getting Started with SQL Server and T-SQL
Preparing for Your Lessons
Summary
3 Working with SQL Server
Making the Connection
Navigating SQL Server Management Studio
Creating the Example Tables
Selecting a Database
Learning About Databases and Tables
Summary
4 Retrieving Data
The SELECT Statement
Retrieving Individual Columns
Retrieving Multiple Columns
Retrieving All Columns

Retrieving Distinct Rows
Limiting Results
Using Fully Qualified Table Names
Summary
5 Sorting Retrieved Data
Sorting Data
Sorting by Multiple Columns
Specifying Sort Direction
Summary
6 Filtering Data
Using the WHERE Clause
The WHERE Clause Operators
Summary
7 Advanced Data Filtering
Combining WHERE Clauses
Using the IN Operator
Using the NOT Operator
Summary
8 Using Wildcard Filtering
Using the LIKE Operator
Tips for Using Wildcards
Summary
9 Creating Calculated Fields
Understanding Calculated Fields
Concatenating Fields
Performing Mathematical Calculations
Summary
10 Using Data Manipulation Functions
Understanding Functions

Using Functions
Summary
11 Summarizing Data
Using Aggregate Functions
Aggregates on Distinct Values
Combining Aggregate Functions
Summary
12 Grouping Data
Understanding Data Grouping
Creating Groups
Filtering Groups
Grouping and Sorting
SELECT Clause Ordering
Summary
13 Working with Subqueries
Understanding Subqueries
Filtering by Subquery
Using Subqueries as Calculated Fields
Checking for Existence with Subqueries
Summary
14 Joining Tables
Understanding Joins
Creating a Join
Summary
15 Creating Advanced Joins
Using Table Aliases
Using Different Join Types
Using Joins with Aggregate Functions
Using Joins and Join Conditions

Summary
16 Combining Queries
Understanding Combined Queries
Creating Combined Queries
Summary
17 Full-Text Searching
Understanding Full-Text Searching
Setting Up Full-Text Searching
Performing Full-Text Searches
Summary
18 Inserting Data
Understanding Data Insertion
Inserting Complete Rows
Inserting Multiple Rows
Inserting Retrieved Data
Reporting On Inserted Data
Summary
19 Updating and Deleting Data
Updating Data
Deleting Data
Reporting On Updated And Deleted Data
Guidelines for Updating and Deleting Data
Summary
20 Creating and Manipulating Tables
Creating Tables
Updating Tables
Deleting Tables
Renaming Tables
Summary

21 Using Views
Understanding Views
Using Views
Summary
22 Programming with T-SQL
Understanding T-SQL Programming
Using Variables
Using Conditional Processing
Grouping Statements
Using Looping
Summary
23 Working with Stored Procedures
Understanding Stored Procedures
Why Use Stored Procedures
Using Stored Procedures
Summary
24 Using Cursors
Understanding Cursors
Working with Cursors
Summary
25 Using Triggers
Understanding Triggers
Using Triggers
Summary
26 Managing Transaction Processing
Understanding Transaction Processing
Controlling Transactions
Summary
27 Working with XML and JSON

Using SQL Server XML Support
Using SQL Server JSON Support
Retrieving Data as JSON
The JSON Functions
Summary
28 Globalization and Localization
Understanding Character Sets and Collation Sequences
Working with Collation Sequences
Managing Case Sensitivity
Working with Unicode
Summary
29 Managing Security
Understanding Access Control
Managing Users
Managing Access Rights
Summary
30 Improving Performance
Improving Performance
Summary
A The Example Tables
Understanding the Example Tables
B T-SQL Statement Syntax
BEGIN TRANSACTION
ALTER TABLE
COMMIT TRANSACTION
CREATE INDEX
CREATE LOGIN
CREATE PROCEDURE

CREATE TABLE
CREATE VIEW
DELETE
DROP
INSERT
INSERT SELECT
ROLLBACK TRANSACTION
SAVE TRANSACTION
SELECT
UPDATE
C T-SQL Datatypes
String Datatypes
Numeric Datatypes
Date and Time Datatypes
Binary Datatypes
Other Datatypes
D T-SQL Reserved Words
Index

About the Author

Ben Forta is Adobe Systems’ Senior Director of Education Initiatives and has

more than three decades of experience in the computer industry in product

development, support, training, and product marketing. He is the author of the

best-selling Sams Teach Yourself SQL in 10 Minutes, spinoff titles (like this one),

the ColdFusion Web Application Construction Kit series, Sams Teach Yourself

Regular Expressions in 10 Minutes, as well as books on Flash, Java, Windows,

and other subjects. He has extensive experience in database design and

development, has implemented databases for several highly successful

commercial software programs and websites, and is a frequent lecturer and

columnist on Internet and database technologies. Ben lives in Oak Park,

Michigan, with his wife Marcy and their children. Ben welcomes your e-mail at

ben@forta.com and invites you to visit his website at http://forta.com/ and to

follow him on Twitter at @benforta.

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Introduction

SQL Server has become one of the most popular database management systems

in the world. From small development projects to some of the best-known and

most prestigious sites on the Web, SQL Server has proven itself to be a solid,

reliable, fast, and trusted solution to all sorts of data-storage needs.

This book is based on my bestselling book Sams Teach Yourself SQL in 10

Minutes, which has become the most-used SQL tutorial in the world, with an

emphasis on teaching what you really need to know—methodically,

systematically, and simply. But as popular and as successful as that book is, it

does have some limitations:

In covering all the major DBMSs, coverage of DBMS-specific features

and functionality had to be kept to a minimum.

To simplify the SQL taught, the lowest common denominator had to be

found—SQL statements that would (as much as possible) work with all

major DBMSs. This requirement necessitated that better DBMS-specific

solutions not be covered.

Although basic SQL tends to be rather portable between DBMSs, more

advanced SQL most definitely is not. As such, that book could not cover

advanced topics, such as triggers, cursors, stored procedures, access

control, transactions, and more in any real detail.

And that is where this book comes in. Sams Teach Yourself Microsoft SQL

Server T-SQL in 10 Minutes builds on the proven tutorials and structure of Sams

Teach Yourself SQL in 10 Minutes, without getting bogged down with anything

but Transact-SQL (T-SQL, for short). It starts with simple data retrieval and

works on to more complex topics, including the use of joins, subqueries, full

text–based searches, functions and stored procedures, cursors, triggers, table

constraints, XML and JSON, and much more. You’ll learn what you need to

know methodically, systematically, and simply—in highly focused lessons

designed to make you immediately and effortlessly productive.

Note: Written for SQL Server 2012 through 2016

This book was written with SQL Server 2012 through 2016 in mind,

and covers features and technologies new to these versions of the

software.

So turn to Lesson 1, “Understanding SQL,” and get to work. You’ll be taking

advantage of all SQL Server has to offer in no time at all.

Who Is This Book For?

This book is for you if...

You are new to SQL.

You are just getting started with SQL Server and want to hit the ground

running.

You want to quickly learn how to get the most out of SQL Server and T-

SQL.

You want to learn how to use T-SQL in your own application development.

You want to be productive quickly and easily using SQL Server without

having to call someone for help.

Companion Website

This book has a companion website online at

http://forta.com/books/0672337924/. Visit the site to access the following:

Table creation and population scripts used to create the sample tables used

throughout this book

The online support forum

Online errata (should one be required)

Other books that may be of interest to you

Conventions Used in This Book

This book uses different typefaces to differentiate between code and regular

English, and also to help you identify important concepts.

Text that you type and text that should appear on your screen is presented in

monospace type. It looks like this to mimic the way text

looks on your screen.

Placeholders for variables and expressions appear in monospace italic

font. You should replace the placeholder with the specific value it represents.

This arrow ( ) at the beginning of a line of code means that a single line of code

is too long to fit on the printed page. Continue typing all the characters after the

as though they were part of the preceding line.

Note

A note presents interesting pieces of information related to the

surrounding discussion.

Tip

A tip offers advice or teaches an easier way to do something.

Caution

A caution advises you about potential problems and helps you steer

clear of disaster.

New Terms

New Term icons provide clear definitions of new, essential terms.

Input

Input identifies code that you can type in yourself. It usually appears next to a

listing.

Output

Output highlights the output produced by running T-SQL code. It usually

appears after a listing.

Analysis

Analysis alerts you to the author’s line-by-line analysis of input or output.

Lesson 1. Understanding SQL

In this lesson, you learn about databases and SQL, prerequisites to learning T-

SQL.

Database Basics

The fact that you are reading this book indicates that you, somehow, need to

interact with databases. So before diving into SQL Server and its T-SQL

implementation of the SQL language, it is important that you understand some

basic concepts about databases and database technologies.

Whether you are aware of it or not, you use databases all the time. Each time you

select a name from your email address book, you are using a database. If you

conduct a search on an Internet search site, you are using a database. When you

log into your network at work, you are validating your name and password

against a database. Even when you use your ATM card at a cash machine, you

are using databases for PIN verification and balance checking.

But even though we all use databases all the time, there remains much confusion

over what exactly a database is. This is especially true because different people

use the same database terms to mean different things. Therefore, a good place to

start our study is with a list and explanation of the most important database

terms.

Tip: Reviewing Basic Concepts

What follows is a very brief overview of some basic database

concepts. It is intended either to jolt your memory, if you already

have some database experience, or to provide you with the absolute

basics, if you are new to databases. Understanding databases is an

important part of mastering SQL Server and T-SQL, and you might

want to find a good book on database fundamentals to brush up on

the subject, if needed.

What Is a Database?

The term database is used in many different ways, but for our purposes a

database is a collection of data stored in some organized fashion. The simplest

way to think of it is to imagine a database as a filing cabinet. The filing cabinet

is simply a physical location to store data, regardless of what that data is or how

it is organized.

New Term: Database

A container (usually a file or set of files) to store organized data.

Caution: Misuse Causes Confusion

People often use the term database to refer to the database software

they are running. This is incorrect, and it is a source of much

confusion. Database software is actually called the Database

Management System (or DBMS). The database is the container

created and manipulated via the DBMS. A database might be a file

stored on a hard drive, but it might not. And for the most part, this is

not even significant because you never access a database directly,

anyway; you always use the DBMS, and it accesses the database for

you.

Tables

When you store information in your filing cabinet, you don’t just toss it in a

drawer. Rather, you create files within the filing cabinet, and then you file related

data in specific files.

In the database world, that file is called a table. A table is a structured file that

can store data of a specific type. A table might contain a list of customers, a

product catalog, or any other list of information.

New Term: Table

A structured list of data of a specific type.

The key here is that the data stored in the table is one type of data or one list.

You would never store a list of customers and a list of orders in the same

database table. Doing so would make subsequent retrieval and access difficult.

Rather, you’d create two tables, one for each list.

Every table in a database has a name that identifies it. That name is always

unique—meaning no other table in that database can have the same name.

Note: Table Names

What makes a table name unique is actually a combination of

several things, including the database name and table name. This

means that although you cannot use the same table name twice in

the same database, you definitely can reuse table names in different

databases.

Tables have characteristics and properties that define how data is stored in them.

These include information about what data may be stored, how it is broken up,

how individual pieces of information are named, and much more. This set of

information that describes a table is known as a schema, and schemas are used to

describe specific tables within a database as well as entire databases (and the

relationship between tables in them, if any).

New Term: Schema

Information about database and table layout and properties.

Columns and Datatypes

Tables are made up of columns. A column contains a particular piece of

information within a table.

New Term: Column

A single field in a table. All tables are made up of one or more

columns.

The best way to understand this is to envision database tables as grids, somewhat

like spreadsheets. Each column in the grid contains a particular piece of

information. In a customer table, for example, one column contains the customer

number, another contains the customer name, and the address, city, state, and ZIP

Code are all stored in their own columns.

Tip: Breaking Up Data

It is extremely important to break data into multiple columns

correctly. For example, city, state, and ZIP Code should always be

separate columns. By breaking these out, it becomes possible to sort

or filter data by specific columns (for example, to find all customers

in a particular state or in a particular city). If city and state are

combined into one column, it would be extremely difficult to sort or

filter by state.

Each column in a database has an associated datatype. A datatype defines what

type of data the column can contain. For example, if the column is to contain a

number (perhaps the number of items in an order), the datatype would be

numeric. If the column were to contain dates, text, notes, currency amounts, and

so on, the appropriate datatype would be used to specify this.

New Term: Datatype

A type of allowed data. Every table column has an associated

datatype that restricts (or allows) specific data in that column.

Datatypes restrict the type of data that can be stored in a column (for example,

preventing the entry of alphabetical characters into a numeric field). Datatypes

also help sort data correctly, and they play an important role in optimizing disk

usage. As such, special attention must be given to picking the right datatype

when tables are created.

Rows

Data in a table is stored in rows; each record saved is stored in its own row.

Again, envisioning a table as a spreadsheet-style grid, the vertical columns in the

grid are the table columns, and the horizontal rows are the table rows.

For example, a customers table might store one customer per row. The number

of rows in the table is the number of records in it.

New Term: Row

A record in a table.

Note: Records or Rows?

You might hear users refer to database records when referring to

rows. For the most part, the two terms are used interchangeably, but

row is technically the correct term.

Primary Keys

Every row in a table should have some column (or set of columns) that uniquely

identifies it. A table containing customers might use a customer number column

for this purpose, whereas a table containing orders might use the order ID. An

employee list table might use an employee ID or the employee Social Security

Number column.

New Term: Primary Key

A column (or set of columns) whose values uniquely identify every

row in a table.

This column (or set of columns) that uniquely identifies each row in a table is

called a primary key. The primary key is used to refer to a specific row. Without

a primary key, updating or deleting specific rows in a table becomes extremely

difficult because there is no guaranteed safe way to refer to just the rows to be

affected.

Tip: Always Define Primary Keys

Although primary keys are not actually required, most database

designers ensure that every table they create has a primary key so

future data manipulation is possible and manageable.

Any column in a table can be established as the primary key, as long as it meets

the following conditions:

No two rows can have the same primary key value.

Every row must have a primary key value (primary key columns may not

allow NULL values).

Tip: Primary Key Rules

The rules listed here are enforced by SQL Server itself.

Primary keys are usually defined on a single column within a table. But this is

not required, and multiple columns may be used together as a primary key. When

multiple columns are used, the rules previously listed must apply to all columns

that make up the primary key, and the values of all columns together must be

unique (individual columns need not have unique values).

Tip: Primary Key Best Practices

In addition to the rules that SQL Server enforces, several universally

accepted best practices should be adhered to:

Don’t update values in primary key columns.

Don’t reuse values in primary key columns.

Don’t use values that might change in primary key columns.

(For example, when you use a name as a primary key to identify

a supplier, you would have to change the primary key when the

supplier merges and changes its name.)

There is another very important type of key called a foreign key, but I’ll get to

that later on in Lesson 14, “Joining Tables.”

What Is SQL?

SQL (pronounced as the letters S-Q-L or as sequel) is an abbreviation for

Structured Query Language. SQL is a language designed specifically for

communicating with databases.

Unlike other languages (spoken languages such as English, or programming

languages such as Java or Visual Basic), SQL is made up of very few words.

This is deliberate. SQL is designed to do one thing and do it well—provide you

with a simple and efficient way to read and write data from a database.

What are the advantages of SQL?

SQL is not a proprietary language used by specific database vendors.

Almost every major DBMS supports SQL, so learning this one language

enables you to interact with just about every database you’ll run into.

SQL is easy to learn. The statements are all made up of descriptive English

words, and there aren’t that many of them.

Despite its apparent simplicity, SQL is actually a very powerful language,

and by cleverly using its language elements you can perform very complex

and sophisticated database operations.

Note: DBMS-Specific SQL

Although SQL is not a proprietary language and a standards

committee exists to define SQL syntax used by all DBMSs, the

reality is that no two DBMSs implement SQL identically. The SQL

taught in this book is T-SQL (Transact-SQL) and is specific to

Microsoft SQL Server, and although much of the language taught

will be usable with other DBMSs, do not assume complete SQL

syntax portability.

Try It Yourself

All the lessons in this book use working examples, showing you the SQL syntax,

what it does, and explaining why it does it. I’d strongly suggest that you try each

and every example for yourself so as to learn T-SQL firsthand. The next two

lessons walk you through getting set up to learn T-SQL.

The lessons in this book use example data that you should download and use.

Appendix A, “The Example Tables,” describes the example tables in detail, and

Lesson 3 shows you how to install them into your SQL Server for use.

Summary

In this first lesson, you learned what SQL is and why it is useful. Because SQL is

used to interact with databases, you also reviewed some basic database

terminology.

Lesson 2. Introducing SQL Server

In this lesson, you learn what SQL Server is, and the tools we’ll be using to work

with it.

What Is SQL Server?

In the previous lesson, you learned about databases and SQL. As explained, it is

the database software (DBMS or Database Management System) that actually

does all the work of storing, retrieving, managing, and manipulating data. SQL

Server is a DBMS—that is, it is database software.

SQL Server has been around for a long time and is in use at millions of

installations worldwide. Why do so many organizations and developers use SQL

Server? Here are some of the reasons:

Performance—SQL Server is fast (make that very fast).

Trusted—SQL Server is used by some of the most important and

prestigious organizations and sites, all of whom entrust it with their critical

data.

Integration—SQL Server is tightly integrated with other Microsoft

offerings as well as the Microsoft Azure cloud.

Simplicity—SQL Server is one of the easiest DBMSs to install and get up

and running, and includes administrative tools that make management of

the server painless and simple.

So why not use SQL Server? First and foremost, SQL Server runs only on

Windows, and if your servers run another operating system (such as Linux), then

obviously you’ll not be able to use SQL Server. In addition, SQL Server is a

commercial product, and for those interested in no-cost open-source offerings,

other DBMSs may be more attractive.

Client Server Software

DBMSs fall into two categories: shared file–based and client/server. The former

(which includes Microsoft Access) are designed for desktop use and are

generally not intended for use on higher-end or more critical applications.

Databases such as SQL Server, Oracle, and MySQL are client/server–based

databases. Client/server applications are split into two distinct parts. The server

portion is a piece of software that is responsible for all data access and

manipulation. This software runs on a computer called the database server.

Only the server software interacts with the data files. All requests for data, data

additions and deletions, and data updates are funneled through the server

software. These requests or changes come from computers running client

software. The client is the piece of software with which the user interacts. If you

request an alphabetical list of products, for example, the client software submits

that request over the network to the server software. The server software

processes the request; filters, discards, and sorts data as necessary; and sends the

results back to your client software.

Note: How Many Computers?

The client and server software may be installed on two computers or

on one computer. Regardless, the client software communicates

with the server software for all database interaction, be it on the

same machine or not.

All this action occurs transparently to you, the user. The fact that data is stored

elsewhere or that a database server is even performing all this processing for you

is hidden. You never need to access the data files directly. In fact, most networks

are set up so that users have no access to the data, or even the drives on which it

is stored.

Why is this significant? Because to work with SQL Server, you need access to

both a computer running the SQL Server software and client software with

which to issue commands to SQL Server:

The server software is the SQL Server DBMS. You can run a locally

installed copy, or you can connect to a copy running on a remote server to

which you have access.

The client can be SQL Server–included tools, scripting languages, web

application development languages, programming languages, and more.

SQL Server Versions

The current version of Microsoft SQL Server is SQL Server 2016 (although prior

versions are in use in many organizations). This book was written with the most

common currently deployed versions of SQL Server in mind, specifically SQL

Server 2016, SQL Server 2014, and SQL Server 2012. That said, much of the

content applies to prior versions as well.

Tip: Use Version 2016

If at all possible, the use of SQL Server 2016 is recommended. Not

only will this make it possible for you to follow along with every

lesson in this book (there are some examples that will only work

with the latest version of SQL Server), but you will also have the

benefit of developing the most current skills.

Note: Version Requirements Noted

Any lesson that requires a specific version of SQL Server is clearly

noted as such at the start of that lesson.

SQL Server Tools

As just explained, SQL Server is a client/server DBMS, so to use SQL Server

you need a client (an application that you use to interact with SQL Server),

which you use to give it commands to be executed (in database parlance the term

execute is used for run).

There are lots of client application options, but when learning SQL Server (and

indeed, when writing and testing SQL Server scripts) you are best off using a

utility designed for simple script execution, and the ideal tool depends on the

version of SQL Server being used. As of SQL Server 2005 Microsoft has been

including a powerful and sophisticated client tool called Microsoft SQL Server

Management Studio. This can be used to create and manage databases and

tables, control database access and security, run wizards to optimize and fine-

tune DBMS performance, and, of course, run SQL statements.

Tip: Local or Remote

Microsoft SQL Server Management Studio can be used to connect

to local or remote DBMSs. So long as the DBMS is configured to

allow you to connect to it, you can connect to any database

anywhere.

Getting Started with SQL Server and T-SQL

If you are new to SQL Server and T-SQL, here is what you need to know to get

started.

Getting Access To a Server

To start using T-SQL and to follow along with the lessons in this book, you need

access to a SQL Server and a client tool. There are three ways to access a server:

The best option is to download and install SQL Server on your own

Windows computer.

If your school or place of work has an installed SQL Server, you may be

able to use that. The server administrator must grant you a server account

(a login name and password) before you’ll be able to use your part of the

server for your own work.

SQL Server can also be hosted on popular cloud platforms, including

Microsoft Azure, Amazon EC2, and Google Cloud.

Regardless of where the server is installed, you need the server address (it’s your

own computer name for a local install) and the login information to access it.

After you’re connected, there’s no difference whatsoever in how you use the

server.

Tip: If You Can, Install a Local Server

For complete control, including access to commands and features

that you will probably not be granted if using someone else’s SQL

Server, install your own local server. Even if you end up using

another local server as your final production DBMS, you’ll still

benefit from having complete and unfettered access to all the server

has to offer.

Regardless of whether you use a local server, you need client software (the

program you use to actually run T-SQL commands), and as previously noted,

Microsoft SQL Server Management Studio is the preferred tool for use with

current versions of SQL Server.

Obtaining the Software

To learn more about Microsoft SQL Server, go to http://www.microsoft.com/sql/.

This page contains links to trial software and other downloads. You may use any

version of SQL Server with this book, including trial versions and the free SQL

Server Express.

Note: SQL Server Express

SQL Server Express is a free version of SQL Server intended for

personal use and for learning SQL. Previous versions of SQL Server

Express were somewhat limited, but this is not the case anymore.

The current SQL Server Express is the exact same software as the

full-blown commercial version of SQL Server; however, it is limited

in the amount of data it can store. But that limit is actually quite

large, and so SQL Server Express is perfectly suited (and priced) for

learning T-SQL with this book.

Installing the Software

Installing SQL Server is straightforward; the installation wizard walks you

through the process, which includes the following options:

Setting an installation location. (The default is usually fine.)

Installing the documentation. (You are strongly encouraged to do so.)

Installing the tools. The most important one is SQL Server Management

Studio, so make sure it is selected and installed.

Selecting from lots of other options. (You can generally use the default

values.)

Note: Locating Microsoft SQL Server Management Studio

If you are using a locally installed copy of SQL Server, then

Microsoft SQL Server Management Studio was probably installed

when the server was installed. If you don’t have SQL Server

Management Studio on your computer, or if you are working with a

remote server, don’t worry. You can download the latest version of

the tool directly from Microsoft’s website, just search for SQL

Server Management Studio.

Preparing for Your Lessons

After you have installed (or received access to) SQL Server, Lesson 3, “Working

with SQL Server,” shows you how to log in and log out of the server, and how to

execute commands.

The lessons in this book all use real T-SQL statements and real data. Appendix

B, “The Example Tables,” describes the example tables used in this book, and

explains how to obtain and use the table-creation and population scripts.

Summary

In this lesson, you learned what exactly SQL Server is, what client tools are, and

what you need to get up and running.

Lesson 3. Working with SQL Server

In this lesson, you learn how to connect and log into SQL Server, how to issue

SQL Server statements, and how to obtain information about databases and

tables. You also create the example tables we’ll be using throughout this book.

Making the Connection

Now that you have a SQL Server DBMS and client software to use with it, it

would be worthwhile to briefly discuss connecting to the database.

SQL Server, like all client/server DBMSs, requires that you log into the DBMS

before you’re able to issue commands. SQL Server can authenticate users and

logins using its own user list or using the Windows user list (the logins used to

start using Windows). As such, depending on how SQL Server is configured, it

may log you in automatically using whatever login you used for Windows itself,

or it may prompt you for a login name and password.

When you first installed SQL Server, you may have been prompted for an

administrative login (often named sa for system administrator) and a password.

If you are using your own local server and are simply experimenting with SQL

Server, using this login is fine. In the real world, however, the administrative

drop entire databases, change logins and passwords, and more.

To connect to SQL Server, you need the following pieces of information:

The hostname (the name of the computer). This is localhost or your

own computer name if you’re connecting to a local SQL Server.

A valid username (if Windows authentication is not being used).

The user password (if required).

Tip: Simplified Local Logins

If you are using a locally installed SQL Server and SQL Server

Management Studio, you should be able to just click the Connect

button when prompted to Connect.

If you’re using one of the client applications discussed in the previous lesson, a

dialog box will be displayed to prompt you for this information.

Note: Using Other Clients

If you are using a client other than Microsoft SQL Server

Management Studio you still need to provide this information in

order to connect to SQL Server.

After you are connected, you have access to whatever databases and tables your

Lesson 29, “Managing Security.”)

Navigating SQL Server Management Studio

Because we’ll be spending so much time in Microsoft SQL Server Management

Studio, a quick tour of the tool is worthwhile:

At the top of the screen is a toolbar containing lots of buttons. One of

those buttons is labeled New Query, and when you click on it you open a

window in which you can type and execute SQL. You’ll be using this

button and window extensively.

The left column is the Object Explorer used to browse databases, tables,

and more. You can right-click on objects to see their details, edit them, and

perform other tasks too. Click on the + next to Databases to see the

databases that have been already created.

On the left of the toolbar, right above the Object Explorer, is a drop-down

box (the first time you open Microsoft SQL Server Management Studio

this box will likely contain the word master). This drop-down box is used

to select the database you want to work with (as you will recall from

Lesson 1, a single DBMS can work with lots of databases). We’ll create

our own database shortly, which will then appear in this drop-down box.

On the right side of the screen is a Properties panel. If it is not visible,

don’t worry, it’ll open when there are properties to be displayed.

Not visible initially is the Results panel. This appears under the Query

window and displays query results when there are results to show.

There are many ways to use Microsoft SQL Server Management Studio, but here

are the basic steps needed to enter and execute SQL statements:

As just noted, the New Query button at the top-left of the screen opens a

window in which SQL statements are entered.

As T-SQL statements are typed, Microsoft SQL Server Management

Studio automatically color-codes the statements and text (this is an

invaluable troubleshooting tool because it lets you quickly spot typos or

missing quotes and so on).

To execute (run) a statement, click the Execute button (the one with the red

exclamation point on it). You can also press F5 or Ctrl+E to execute a

statement.

To verify that a SQL statement is syntactically correct (without executing

it), click the Parse button (the one with the blue checkmark on it).

Microsoft SQL Server Management Studio displays statement results at

the bottom of the screen. Results may be displayed in a grid (the default

behavior), as plain text, or saved to a file. You can switch between these

modes by clicking the appropriate toolbar buttons.

In addition to displaying statement results, Microsoft SQL Server

Management Studio also displays status messages (the number of rows

returned, for example) in a second tab labeled Messages.

To obtain help, click the statement you need help with and press F1.

Creating the Example Tables

In order to follow along with the examples, you need the example database

tables. Everything you need to get up and running can be found on this book’s

web page at http://www.forta.com/books/0672337924/.

The web page contains two SQL script files that you may download. These files

are plain text files that contain SQL statement that create and populate the

example tables we’ll be using. Download both files:

create.sql contains the T-SQL statements to create the six database

tables (including the statements to define all primary keys and foreign key

constraints).

populate.sql contains the SQL INSERT statements used to populate

these tables.

Note: For SQL Server Only

The SQL statements in the downloadable .sql files are very

DBMS-specific and are designed to be used only with Microsoft

SQL Server.

The scripts have been tested extensively with SQL Server 2012 and

later, and have not been tested with earlier versions of SQL Server.

Note: Create, Then Populate

You must run the table-creation scripts before the table-population

scripts. Be sure to check for any error messages returned by these

scripts. If the creation scripts fail, you will need to remedy whatever

problem might exist before continuing with table population.

After you have downloaded the scripts, you can use them to create and populate

the tables needed to follow along with the lessons in this book. Here are the steps

to follow:

1. To be safe, we’ll use a dedicated database to learn T-SQL, this way you

won’t be able to mistakenly access or change other data. In Microsoft SQL

Server Management Studio, right click on Databases in the Object

Explorer and select New Database . . . to display the New Database dialog.

Specify learnsql as your new database name (or pick any name of your

choice, but do not use any existing database name, just to be on the safe

side). You can ignore all other options in the dialog box and just click OK

to create the database.

2. Now that you have a database, you can create the example tables. Click

New Query to open a query window. Make sure the new database is

selected from the list of databases from the drop-down list (or you’ll create

tables in the wrong database).

3. First you need to execute the create.sql script. You may simply copy

and paste the entire contents of the file into the query window, or you can

use the File menu options to open create.sql directly. When you have

the SQL code in the Query Window, click the Execute button to run the

script and create the tables.

4. Repeat the previous step using the populate.sql file to populate the

new tables. Again, make sure you have the right database selected.

And with that you should be good to go!

Selecting a Database

As you’ve just seen, when you first connect to SQL Server, a default database is

opened for you. This is usually a database named master (which as a rule you

should never play with). Before you perform any database operations, you need

to select the appropriate database. You did this using the drop-down box in the

toolbar, but you can also select the database using the SQL USE keyword.

New Term: Keyword

A reserved word that is part of the T-SQL language. Never name a

table or column using a keyword. Appendix E, “T-SQL Reserved

Words,” lists the SQL Server keywords.

For example, to use the learnsql database, you would enter the following (in

a query window) and execute it:

Input

USE learnsql;

Tip: SQL Server Management Studio Is Smart

SQL Server Management Studio helps you write SQL code. In

addition to the color-coding, which you undoubtedly noticed, it also

tries to give you options so that you write less code (and make

fewer mistakes). As such, when you typed the USE statement it

popped up an inline database list for selection. You’ll see lots of this

type of help and behavior when using the tool.

Output

Click here to view code image

Command(s) completed successfully.

Analysis

The USE statement does not return any results, but you will see a message

confirming that the command was successfully completed.

Tip: USE Or Interactive Database Selection?

In SQL Server Management Studio you may select a database from

the drop-down list in the toolbar to use it. So why do you need the

USE statement? Well, although you’ll not actually see the USE

command being issued when making a selection from the Database

drop-down, SQL Server Management Studio is using USE internally

to select the database, just as you did in the Query window. So even

if you use the drop-down list, you are still using the USE command.

Remember, you must always USE a database before you can access any data in

it.

Learning About Databases and Tables

But what if you don’t know the names of the available databases? And for that

matter, how do the client applications obtain the list of available databases that

are displayed in the drop-down list?

Information about databases, tables, columns, users, privileges, and more, are

stored within databases and tables themselves (yes, SQL Server uses SQL Server

to store this information). These internal tables are all in the master database

(which is why you don’t want to tamper with it), and they are generally not

accessed directly. Instead, SQL Server includes a suite of prewritten stored

procedures that can be used to obtain this information (information that SQL

Server then extracts from those internal tables).

Note: Stored Procedures

Stored procedures will be covered in Lesson 23, “Working with

Stored Procedures.” For now, it suffices to say that stored

procedures are SQL statements that are saved in SQL Server and

can be executed as needed.

Look at the following example:

Input

sp_databases;

Output

Click here to view code image

DATABASE_NAME DATABASE_SIZE REMARKS

----------------- ------------- -------

coldfusion 9096 NULL

learnsql 3072 NULL

forta 2048 NULL

master 4608 NULL

model 1728 NULL

msdb 5824 NULL

tempdb 8704 NULL

Analysis

sp_databases; returns a list of available databases. Included in this list

might be databases used by SQL Server internally (such as master and

tempdb in this example). Of course, your own list of databases might not look

like those shown above.

To obtain a list of tables within a database, make sure you are using the right

database, and then use sp_tables;, as seen here:

Input

sp_tables;

Analysis

sp_tables; returns a list of available tables in the currently selected database,

and not just your tables; it also includes all sorts of system tables and other

entries (possibly hundreds of entries).

To obtain a list of tables (just tables, not views, and not system tables and so on),

you can use this statement:

Input

Click here to view code image

sp_tables NULL, dbo, learnsql, "'TABLE'";

Output

Click here to view code image

TABLE_QUALIFIER TABLE_OWNER TABLE_NAME TABLE_TYPE REMARKS

--------------- ----------- ------------ ---------- -------

crashcourse dbo customers TABLE NULL

crashcourse dbo orderitems TABLE NULL

crashcourse dbo orders TABLE NULL

crashcourse dbo products TABLE NULL

crashcourse dbo vendors TABLE NULL

crashcourse dbo productnotes TABLE NULL

crashcourse dbo sysdiagrams TABLE NULL

Analysis

Here, sp_tables accepts a series of parameters telling it which database to

use, as well as what specifically to list ('TABLE' as opposed to 'VIEW' or

'SYSTEM TABLE').

sp_columns can be used to display a table’s columns:

Input

sp_columns customers;

Note: Shortened for Brevity

sp_columns returns lots of data. In the output that follows, I

truncated the display because the full output would have been far

wider than the pages in this book, likely requiring many lines for

each row.

Output

Click here to view code image

TABLE_QUALIFIER TABLE_OWNER TABLE_NAME COLUMN_NAME DATA_TYPE

TYPE_NAME

learnsql dbo customers cust_id 4 int

identity

learnsql dbo customers cust_name -8 nchar

learnsql dbo customers cust_address -8 nchar

learnsql dbo customers cust_city -8 nchar

learnsql dbo customers cust_state -8 nchar

learnsql dbo customers cust_zip -8 nchar

learnsql dbo customers cust_country -8 nchar

learnsql dbo customers cust_contact -8 nchar

learnsql dbo customers cust_email -8 nchar

Analysis

sp_columns requires that a table name be specified (customers in this

example), and returns a row for each field, containing the field name, its

datatype, whether NULL is allowed, key information, default value, and much

more.

Note: What Is Identity

Column cust_id is an identity column. Some table columns

need unique values (for example, order numbers, employee IDs, or,

as in the example just shown, customer IDs). Rather than have to

assign unique values manually each time a row is added (and having

to keep track of what value was last used), SQL Server can

automatically assign the next available number for you each time a

row is added to a table. This functionality is known as identity. If it

is needed, it must be part of the table definition used when the table

is created using the CREATE statement. We’ll look at CREATE in

Lesson 20, “Creating and Manipulating Tables.”

Lots of other stored procedures are supported, too, including:

sp_server_info—Used to display extensive server status information

sp_spaceused—Used to display the amount of space used (and

unused) by a database

sp_statistics—Used to display usage statistics pertaining to

database tables

sp_helpuser—Used to display available user accounts

sp_helplogins—Used to display user logins and what they have

rights to

It is worthwhile to note that client applications use these same stored procedures

you’ve seen here. Applications that display interactive lists of databases and

tables, that allow for the interactive creation and editing of tables, that facilitate

data entry and editing, or that allow for user account and rights management, and

more, all accomplish what they do using the same stored procedures that you can

execute directly yourself.

Summary

In this lesson, you learned how to connect and log into SQL Server, how to

select databases using USE, and how to introspect SQL databases, tables, and

internals using stored procedures. Armed with this knowledge, you can now dig

into the all-important SELECT statement.

Lesson 4. Retrieving Data

In this lesson, you learn how to use the SELECT statement to retrieve one or

more columns of data from a table.

The SELECT Statement

As explained in Lesson 1, “Understanding SQL,” SQL statements are made up

of plain English terms. These terms are called keywords, and every SQL

statement is made up of one or more keywords. The SQL statement you probably

use most frequently is the SELECT statement. Its purpose is to retrieve

information from one or more tables.

To use SELECT to retrieve table data, at a minimum you must specify two

pieces of information—what you want to select and from where you want to

select it.

Note: Make Sure the Right Database Is in Use

In order to follow along with the examples, make sure that you are

using the right database (the one in which you created and

populated the example tables).

Retrieving Individual Columns

We’ll start with a simple SQL SELECT statement, as follows:

Input

SELECT prod_name

FROM products;

Analysis

The previous statement uses the SELECT statement to retrieve a single column

called prod_name from the products table. The desired column name is

specified right after the SELECT keyword, and the FROM keyword specifies the

name of the table from which to retrieve the data. The output from this statement

is shown here:

Output

prod_name

--------------

.5 ton anvil

1 ton anvil

2 ton anvil

Detonator

Bird seed

Carrots

Fuses

JetPack 1000

JetPack 2000

Oil can

Safe

Sling

TNT (1 stick)

TNT (5 sticks)

Note: Unsorted Data

If you tried this query yourself, you might have discovered that the

data was displayed in a different order than shown here. If this is the

case, don’t worry—it is working exactly as it is supposed to. If

query results are not explicitly sorted (we’ll get to that in the next

lesson), data will be returned in no order of any significance. It

might be the order in which the data was added to the table, but it

might not. As long as your query returned the same number of rows,

it is working.

A simple SELECT statement like the one just shown returns all the rows in a

table. Data is not filtered (so as to retrieve a subset of the results), nor is it sorted.

We’ll discuss these topics in the next few lessons.

Note: Terminating Statements

Multiple SQL statements must be separated by semicolons (the ;

character). SQL Server (like most DBMSs) does not require that a

semicolon be specified after single statements. Of course, you can

always add a semicolon if you wish. It does no harm, even if it isn’t

needed.

Note: SQL Statements and Case

It is important to note that SQL statements are not case sensitive.

Therefore, SELECT is the same as select, which is the same as

Select. Many SQL developers find that using uppercase for all

SQL keywords and lowercase for column and table names makes

code easier to read and debug. As a best practice, pick a case

convention and use it consistently.

Tip: Use of White Space

All extra white space within a SQL statement is ignored when that

statement is processed. SQL statements can be specified on one long

line or broken up over many lines. Most SQL developers find that

breaking up statements over multiple lines makes them easier to

read and debug.

Retrieving Multiple Columns

To retrieve multiple columns from a table, the same SELECT statement is used.

The only difference is that multiple column names must be specified after the

SELECT keyword, and each column must be separated by a comma.

Tip: Take Care with Commas

When selecting multiple columns, be sure to specify a comma

between each column name, but not after the last column name.

Doing so generates an error.

The following SELECT statement retrieves three columns from the products

table:

Input

Click here to view code image

SELECT prod_id, prod_name, prod_price

FROM products;

Analysis

Just as in the prior example, this statement uses the SELECT statement to

retrieve data from the products table. In this example, three column names

are specified, each separated by a comma. The output from this statement is as

follows:

Output

Click here to view code image

prod_id prod_name prod_price

---------- ---------------- ----------

ANV01 .5 ton anvil 5.99

ANV02 1 ton anvil 9.99

ANV03 2 ton anvil 14.99

DTNTR Detonator 13.00

FB Bird seed 10.00

FC Carrots 2.50

FU1 Fuses 3.42

JP1000 JetPack 1000 35.00

JP2000 JetPack 2000 55.00

OL1 Oil can 8.99

SAFE Safe 50.00

SLING Sling 4.49

TNT1 TNT (1 stick) 2.50

TNT2 TNT (5 sticks) 10.00

Note: Presentation of Data

SQL statements typically return raw, unformatted data. Data

formatting is a presentation issue, not a retrieval issue. Therefore,

presentation (for example, alignment and displaying price values as

currency amounts with the currency symbol and commas) is

typically specified in the application that displays the data. Actual

raw retrieved data (without application-provided formatting) is

rarely displayed as is.

Retrieving All Columns

In addition to being able to specify desired columns (one or more, as shown

previously), you can use SELECT statements to request all columns without

having to list them individually. This is done using the asterisk (*) wildcard

character in lieu of actual column names, as follows:

Input

SELECT *

FROM products;

Analysis

When a wildcard (*) is specified, all the columns in the table are returned. The

columns are in the order in which they appear in the table definition. However,

this cannot be relied on because changes to table schemas (adding and removing

columns, for example) could cause ordering changes.

Caution: Using Wildcards

As a rule, you are better off not using the * wildcard unless you

really do need every column in the table. Even though use of

wildcards might save you the time and effort needed to list the

desired columns explicitly, retrieving unnecessary columns usually

slows down the performance of your retrieval and your application.

Tip: Retrieving Unknown Columns

There is one big advantage to using wildcards. Because you do not

explicitly specify column names (because the asterisk retrieves

every column), it is possible to retrieve columns whose names are

unknown.

Retrieving Distinct Rows

As you have seen, SELECT returns all matched rows. But what if you did not

want every occurrence of every value? For example, suppose you want the

vendor ID of all vendors with products in your products table:

Input

SELECT vend_id

FROM products;

Output

vend_id

-----------

1001

1003

1002

1005

1002

1003

The SELECT statement returned 14 rows (even though only four vendors are in

that list) because there are 14 products listed in the products table. So how

could you retrieve a list of distinct values?

The solution is to use the DISTINCT keyword, which, as its name implies,

instructs SQL Server to only return distinct values:

Input

SELECT DISTINCT vend_id

FROM products;

Analysis

SELECT DISTINCT vend_id tells SQL. Server to only return distinct

(unique) vend_id rows, and therefore only four rows are returned, as shown in

the following output. If used, the DISTINCT keyword must be placed directly

in front of the column names.

Output

vend_id

-----------

1001

1002

1003

1005

Caution: Can’t Be Partially DISTINCT

The DISTINCT keyword applies to all columns, not just the one it

precedes. If you were to specify SELECT DISTINCT vend_id,

prod_price, all rows would be retrieved unless both of the

specified columns were distinct.

Limiting Results

SELECT statements return all matched rows, possibly every row in the specified

table. To return to just the first row or rows, use the TOP keyword. Here is an

example:

Input

SELECT TOP(5) prod_name

FROM products;

Analysis

The previous statement uses the SELECT statement to retrieve a single column.

TOP(5) instructs SQL Server to return no more than five rows. The output from

this statement is shown next:

Output

prod_name

------------

.5 ton anvil

1 ton anvil

2 ton anvil

Detonator

Bird seed

Note: SQL Server 6.5 or Later

Support for TOP was introduced into T-SQL in SQL Server 6.5. If

you are using an earlier version of SQL Server, use SET

ROWCOUNT instead.

You can also use TOP to get a percentage of rows by adding the keyword

PERCENT. Here is an example:

Input

Click here to view code image

SELECT TOP(25)PERCENT prod_name

FROM products;

Analysis

TOP(25) PERCENT instructs SQL Server to return the first 25 percent of rows

in the products table. The output from this statement is shown here:

Output

prod_name

-------------

.5 ton anvil

1 ton anvil

2 ton anvil

Detonator

Note: When There Aren’t Enough Rows

The number of rows to retrieve as specified in TOP is the maximum

number to retrieve. If there aren’t enough rows (for example, you

specified TOP(20), but there were only 14 rows), SQL Server

returns as many as it can.

Using Fully Qualified Table Names

The SQL examples used thus far have referred to columns by just the column

names. It is also possible to refer to columns using fully qualified names (using

both the table and column names). Look at this example:

Input

SELECT products.prod_name

FROM products;

This SQL statement is functionally identical to the very first one used in this

lesson, but here a fully qualified column name is specified.

Table names, too, may be fully qualified, as shown here:

Input

SELECT products.prod_name

FROM learnsql.dbo.products;

Once again, this statement is functionally identical to the one just used

(assuming, of course, that the products table is indeed in the learnsql

database). But notice the extra dbo in the table name. A fully qualified table

name is made up of a database name, the table owner name, and the table name.

The default owner will always be dbo (as in database owner), and therefore

learnsql.dbo.products is the fully qualified name for table products

in the learnsql database.

In certain situations, fully qualified names are required, as you will see in later

lessons. For now, it is worth noting this syntax so you’ll know what it is if you

run across it.

Summary

In this lesson, you learned how to use the SQL SELECT statement to retrieve a

single table column, multiple table columns, and all table columns. Next you

learn how to sort the retrieved data.

Lesson 5. Sorting Retrieved Data

In this lesson, you learn how to use the SELECT statement’s ORDER BY clause

to sort retrieved data as needed.

Sorting Data

As you learned in the last lesson, the following SQL statement returns a single

column from a database table. But look at the output. The data appears to be

displayed in no particular order at all.

Input

SELECT prod_name

FROM products;

Output

prod_name

--------------

.5 ton anvil

1 ton anvil

2 ton anvil

Detonator

Bird seed

Carrots

Fuses

JetPack 1000

JetPack 2000

Oil can

Safe

Sling

TNT (1 stick)

TNT (5 sticks)

Actually, the retrieved data is not displayed in a mere random order. If unsorted,

data is typically displayed in the order in which it appears in the underlying

tables. This could be the order in which the data was added to the tables initially.

However, if data was subsequently updated or deleted, the order is affected by

how SQL Server reuses reclaimed storage space. The end result is that you

cannot (and should not) rely on the sort order if you do not explicitly control it.

Relational database design theory states that the sequence of retrieved data

cannot be assumed to have significance if ordering is not explicitly specified.

New Term: Clause

SQL statements are made up of clauses, some required and some

optional. A clause usually consists of a keyword and supplied data.

An example of this is the SELECT statement’s FROM clause,

which you saw in the last lesson.

To explicitly sort data retrieved using a SELECT statement, you use the ORDER

BY clause. ORDER BY takes the name of one or more columns by which to sort

the output. Look at the follxowing example:

Input

SELECT prod_name

FROM products

ORDER BY prod_name;

Analysis

This statement is identical to the earlier statement, except it also specifies an

ORDER BY clause instructing SQL Server to sort the data alphabetically by the

prod_name column. The results are as follows:

Output

prod_name

--------------

.5 ton anvil

1 ton anvil

2 ton anvil

Bird seed

Carrots

Detonator

Fuses

JetPack 1000

JetPack 2000

Oil can

Safe

Sling

TNT (1 stick)

TNT (5 sticks)

Tip: Sorting by Nonselected Columns

More often than not, the columns used in an ORDER BY clause are

ones that were selected for display. However, this is actually not

required, and it is perfectly legal to sort data by a column that is not

retrieved.

Sorting by Multiple Columns

It is often necessary to sort data by more than one column. For example, if you

are displaying an employee list, you might want to display it sorted by last name

and first name (first sort by last name, and then within each last name sort by

first name). This would be useful if multiple employees have the same last name.

To sort by multiple columns, simply specify the column names separated by

commas (just as you do when you are selecting multiple columns).

The following code retrieves three columns and sorts the results by two of them

—first by price and then by name.

Input

Click here to view code image

SELECT prod_id, prod_price, prod_name

FROM products

ORDER BY prod_price, prod_name;

Output

Click here to view code image

prod_id prod_price prod_name

---------- --------------------- --------------

FC 2.50 Carrots

TNT1 2.50 TNT (1 stick)

FU1 3.42 Fuses

SLING 4.49 Sling

ANV01 5.99 .5 ton anvil

OL1 8.99 Oil can

ANV02 9.99 1 ton anvil

FB 10.00 Bird seed

TNT2 10.00 TNT (5 sticks)

DTNTR 13.00 Detonator

ANV03 14.99 2 ton anvil

JP1000 35.00 JetPack 1000

SAFE 50.00 Safe

JP2000 55.00 JetPack 2000

It is important to understand that when you are sorting by multiple columns, the

sort sequence is exactly as specified. In other words, using the output in the

previous example, the products are sorted by the prod_name column only

when multiple rows have the same prod_price value. If all the values in the

prod_price column had been unique, no data would have been sorted by

prod_name.

Specifying Sort Direction

Data sorting is not limited to ascending sort orders (from A to Z). Although this

is the default sort order, the ORDER BY clause can also be used to sort in

descending order (from Z to A). To sort by descending order, you must specify

the keyword DESC.

The following example sorts the products by price in descending order (most

expensive first):

Input

Click here to view code image

SELECT prod_id, prod_price, prod_name

FROM products

ORDER BY prod_price DESC;

Output

Click here to view code image

prod_id prod_price prod_name

---------- --------------------- --------------

JP2000 55.00 JetPack 2000

SAFE 50.00 Safe

JP1000 35.00 JetPack 1000

ANV03 14.99 2 ton anvil

DTNTR 13.00 Detonator

TNT2 10.00 TNT (5 sticks)

FB 10.00 Bird seed

ANV02 9.99 1 ton anvil

OL1 8.99 Oil can

ANV01 5.99 .5 ton anvil

SLING 4.49 Sling

FU1 3.42 Fuses

FC 2.50 Carrots

TNT1 2.50 TNT (1 stick)

But what if you were to sort by multiple columns, how would that impact sort

direction? The following example sorts the products in descending order (most

expensive first), plus provides the product name:

Input

Click here to view code image

SELECT prod_id, prod_price, prod_name

FROM products

ORDER BY prod_price DESC, prod_name;

Output

Click here to view code image

prod_id prod_price prod_name

---------- --------------------- --------------

JP2000 55.00 JetPack 2000

SAFE 50.00 Safe

JP1000 35.00 JetPack 1000

ANV03 14.99 2 ton anvil

DTNTR 13.00 Detonator

FB 10.00 Bird seed

TNT2 10.00 TNT (5 sticks)

ANV02 9.99 1 ton anvil

OL1 8.99 Oil can

ANV01 5.99 .5 ton anvil

SLING 4.49 Sling

FU1 3.42 Fuses

FC 2.50 Carrots

TNT1 2.50 TNT (1 stick)

Analysis

The DESC keyword only applies to the column name that directly precedes it. In

the previous example, DESC was specified for the prod_price column, but

not for the prod_name column. Therefore, the prod_price column is sorted

in descending order, but the prod_name column (within each price) is still

sorted in standard ascending order.

Tip: Sorting Descending on Multiple Columns

If you want to sort in descending order on multiple columns, be sure

each column has its own DESC keyword.

The opposite of DESC is ASC (for ascending), which may be specified to sort in

ascending order. In practice, however, ASC is not generally used because

ascending order is the default sequence (and is assumed if neither ASC nor DESC

is specified).

Tip: Case Sensitivity and Sort Orders

When you are sorting textual data, is A the same as a? And does a

come before B or after Z? These are not theoretical questions, and

the answers depend on how the database is set up.

In dictionary sort order, A is treated the same as a, and that is the

default behavior in SQL Server (and indeed most DBMSs).

However, administrators can change this behavior if needed. (If

your database contains lots of foreign language characters, this

might become necessary.)

The key here is that, if you do need an alternate sort order, you

cannot accomplish it with a simple ORDER BY clause. You must

contact your database administrator.

Using a combination of ORDER BY and TOP, it is possible to find the highest or

lowest value in a column. The following example demonstrates how to find the

value of the most expensive item:

Input

SELECT TOP(1) prod_price

FROM products

ORDER BY prod_price DESC;

Output

prod_price

---------------------

55.00

Analysis

prod_price DESC ensures that rows are retrieved from most to least

expensive, and TOP(1) tells SQL Server to just return one row.

Caution: Position of the ORDER BY Clause

When specifying an ORDER BY clause, be sure that it is after the

FROM clause. Using clauses out of order generates an error

message.

Summary

In this lesson, you learned how to sort retrieved data using the SELECT

statement’s ORDER BY clause. This clause, which must be the last in the

SELECT statement, can be used to sort data on one or more columns, as needed.

Lesson 6. Filtering Data

In this lesson, you learn how to use the SELECT statement’s WHERE clause to

specify search conditions.

Using the WHERE Clause

Database tables usually contain large amounts of data, and you seldom need to

retrieve all the rows in a table. More often than not, you’ll want to extract a

subset of the table’s data as needed for specific operations or reports. Retrieving

just the data you want involves specifying search criteria, also known as filter

conditions.

Within a SELECT statement, you filter data by specifying search criteria in the

WHERE clause. The WHERE clause is specified right after the table name (using

the FROM clause) as follows:

Input

SELECT prod_name, prod_price

FROM products

WHERE prod_price = 2.50;

Analysis

This statement retrieves two columns from the products table, but instead of

returning all rows, it returns only rows with a prod_price value of 2.50, as

shown here:

Output

prod_name prod_price

-------------- ----------

Carrots 2.50

TNT (1 stick) 2.50

This example uses a simple equality test: It checks to see if a column has a

specified value, and it filters the data accordingly. However, T-SQL enables you

to do more than just test for equality.

Tip: SQL versus Application Filtering

Data can also be filtered at the application level. To do this, the SQL

SELECT statement retrieves more data than is actually required for

the client application, and the client code loops through the returned

data to extract just the needed rows.

As a rule, this practice is strongly discouraged. DBMSs are

optimized to perform filtering quickly and efficiently. Making the

client application (or development language) do the database’s job

dramatically impacts application performance and creates

applications that cannot scale properly. In addition, if data is filtered

at the client, the server has to send unneeded data across the

network connections, resulting in a waste of network bandwidth

resources.

Caution: WHERE Clause Position

When using both ORDER BY and WHERE clauses, make sure

ORDER BY comes after WHERE; otherwise, an error will be

generated. (See Lesson 5, “Sorting Retrieved Data,” for more

information on using ORDER BY.)

The WHERE Clause Operators

The first WHERE clause we looked at tests for equality—determining if a column

contains a specific value. T-SQL supports a whole range of comparison

operators, some of which are listed in Table 6.1.

TABLE 6.1 WHERE Clause Operators

Checking Against a Single Value

You have already seen an example of testing for equality. Here’s one more:

Input

SELECT prod_name, prod_price

FROM products

WHERE prod_name ='fuses';

Output

prod_name prod_price

----------- ----------

Fuses 3.42

Analysis

Checking for WHERE prod_name = 'fuses' returned a single row with a

value of Fuses. By default, T-SQL is not case sensitive when performing

matches, and therefore fuses and Fuses matched.

Now we’ll look at a few examples to demonstrate the use of other operators.

This first example lists all products that cost less than 10:

Input

SELECT prod_name, prod_price

FROM products

WHERE prod_price < 10;

Output

prod_name prod_price

-------------- ----------

.5 ton anvil 5.99

1 ton anvil 9.99

Carrots 2.50

Fuses 3.42

Oil can 8.99

Sling 4.49

TNT (1 stick) 2.50

This next statement retrieves all products costing 10 or less (resulting in two

additional matches):

Input

SELECT prod_name, prod_price

FROM products

WHERE prod_price <= 10;

Output

prod_name prod_price

-------------- ----------

.5 ton anvil 5.99

1 ton anvil 9.99

Bird seed 10.00

Carrots 2.50

Fuses 3.42

Oil can 8.99

Sling 4.49

TNT (1 stick) 2.50

TNT (5 sticks) 10.00

Checking for Nonmatches

This example lists all products not made by vendor 1003:

Input

SELECT vend_id, prod_name

FROM products

WHERE vend_id <> 1003;

Output

vend_id prod_name

----------- ------------

1001 .5 ton anvil

1001 1 ton anvil

1001 2 ton anvil

1002 Fuses

1005 JetPack 1000

1005 JetPack 2000

1002 Oil can

Tip: When to Use Quotes

If you look closely at the conditions used in the examples’ WHERE

clauses, you will notice that some values are enclosed within single

quotes (such as 'fuses', used previously) and others are not. The

single quotes are used to delimit strings. If you are comparing a

value against a column that is a string datatype, the delimiting

quotes are required. Quotes are not used to delimit values used with

numeric columns.

The following is the same example, except this one uses the != operator instead

of <>:

Input

SELECT vend_id, prod_name

FROM products

WHERE vend_id != 1003;

Checking for a Range of Values

To check for a range of values, you can use the BETWEEN operator. Its syntax is

a little different from other WHERE clause operators because it requires two

values: the beginning and end of the range. The BETWEEN operator can be used,

for example, to check for all products that cost between 5 and 10 or for all dates

that fall between specified start and end dates.

The following example demonstrates the use of the BETWEEN operator by

retrieving all products with a price between 5 and 10:

Input

Click here to view code image

SELECT prod_name, prod_price

FROM products

WHERE prod_price BETWEEN 5 AND 10;

Output

prod_name prod_price

---------------- ----------

.5 ton anvil 5.99

1 ton anvil 9.99

Bird seed 10.00

Oil can 8.99

TNT (5 sticks) 10.00

Analysis

As shown in this example, when BETWEEN is used, two values must be specified

—the low end and high end of the desired range. The two values must also be

separated by the AND keyword. BETWEEN matches all the values in the range,

including the specified range start and end values.

Checking for No Value

When a table is created, the table designer can specify whether individual

columns can contain no value. When a column contains no value, it is said to

contain a NULL value.

New Term: NULL

No value, as opposed to a field containing 0, an empty string, or just

spaces.

The SELECT statement has a special WHERE clause that can be used to check for

columns with NULL values—the IS NULL clause. The syntax looks like this:

Input

SELECT prod_name

FROM products

WHERE prod_price IS NULL;

This statement returns a list of all products that have no price (an empty

prod_price field, not a price of 0), and because there are none, no data is

returned. The customers table, however, does contain columns with NULL

values—the cust_email column contains NULL if a customer has no email

address on file:

Input

SELECT cust_id

FROM customers

WHERE cust_email IS NULL;

Output

cust_id

-----------

10002

110005

Caution: NULL and Nonmatches

You might expect that when you filter to select all rows that do not

have a particular value, rows with a NULL will be returned. But they

will not. Because of the special meaning of unknown, the database

does not know whether these rows match, so they are not returned

when filtering for matches or when filtering for nonmatches.

When filtering data, make sure to verify that the rows with a NULL

in the filtered column are really present in the returned data.

Summary

In this lesson, you learned how to filter returned data using the SELECT

statement’s WHERE clause. You learned how to test for equality, nonequality,

greater than and less than, value ranges, and NULL values.

Lesson 7. Advanced Data Filtering

In this lesson, you learn how to combine WHERE clauses to create powerful and

sophisticated search conditions. You also learn how to use the NOT and IN

operators.

Combining WHERE Clauses

All the WHERE clauses introduced in Lesson 6, “Filtering Data,” filter data using

a single criteria. For a greater degree of filter control, T-SQL allows you to

specify multiple WHERE clauses. These clauses may be used in two ways: as

AND clauses or as OR clauses.

New Term: Operator

A special keyword used to join or change clauses within a WHERE

clause. Also known as a logical operator.

Using the AND Operator

To filter by more than one column, you use the AND operator to append

conditions to your WHERE clause. The following code demonstrates this:

Input

Click here to view code image

SELECT prod_id, prod_price, prod_name

FROM products

WHERE vend_id = 1003 AND prod_price <= 10;

Analysis

The preceding SQL statement retrieves the product name and price for all

products made by vendor 1003 as long as the price is 10 or less. The WHERE

clause in this SELECT statement is made up of two conditions, and the keyword

AND is used to join them. AND instructs SQL Server to return only rows that

meet all the conditions specified. If a product is made by vendor 1003 but it

costs more than 10, it is not retrieved. Similarly, products that cost less than 10

that are made by a vendor other than the one specified are not retrieved. The

output generated by this SQL statement is as follows:

Output

Click here to view code image

prod_id prod_price prod_name

---------- --------------------- ---------------

FB 10.00 Bird seed

FC 2.50 Carrots

SLING 4.49 Sling

TNT1 2.50 TNT (1 stick)

TNT2 10.00 TNT (5 sticks)

New Term: AND

A keyword used in a WHERE clause to specify that only rows

matching all the specified conditions should be retrieved.

The current example contains a single AND clause and is thus made up of two

filter conditions. Additional filter conditions can be used as well, each seperated

by an AND keyword.

Using the OR Operator

As just seen, AND requires that both conditions be met for a row to be retrieved.

The OR operator instructs SQL Server to retrieve rows that match either

condition, so if one matches and one does not, the row would still be retrieved.

Look at the following SELECT statement:

Input

Click here to view code image

SELECT prod_name, prod_price

FROM products

WHERE vend_id = 1002 OR vend_id = 1003;

Analysis

This SQL statement retrieves the product name and price for any products made

by either of the two specified vendors. The OR operator tells the DBMS to match

either condition, not necessarily both. If an AND operator would have been used

here, no data would be returned (it would have created a WHERE clause that

could never be matched). The output generated by this SQL statement is as

follows:

Output

prod_name prod_price

--------------- ----------

Detonator 13.00

Bird seed 10.00

Carrots 2.50

Fuses 3.42

Oil can 8.99

Safe 50.00

Sling 4.49

TNT (1 stick) 2.50

TNT (5 sticks) 10.00

New Term: OR

A keyword used in a WHERE clause to specify that any rows

matching either of the specified conditions should be retrieved.

Understanding Order of Evaluation

WHERE clauses can contain any number of AND and OR operators. Combining

the two enables you to perform sophisticated and complex filtering.

However, combining AND and OR operators presents an interesting problem. To

demonstrate this, let’s look at an example. Suppose you need a list of all

products costing 10 or more made by vendors 1002 and 1003. The following

SELECT statement uses a combination of AND and OR operators to build a

WHERE clause:

Input

Click here to view code image

SELECT prod_name, prod_price

FROM products

WHERE vend_id = 1002 OR vend_id = 1003 AND prod_price >= 10;

Output

prod_name prod_price

---------------- ----------

Detonator 13.00

Bird seed 10.00

Fuses 3.42

Oil can 8.99

Safe 50.00

TNT (5 sticks) 10.00

Analysis

Look at the listed results. Two of the rows returned have prices less than 10—

so, obviously, the rows were not filtered as intended. Why did this happen? The

answer is the order of evaluation. T-SQL (like most languages) processes AND

operators before OR operators. When SQL Server sees the preceding WHERE

clause, it reads products made by vendor 1002 regardless of price, and any

products costing 10 or more made by vendor 1003. In other words, because

AND ranks higher in the order of evaluation, the wrong operators were joined

together.

The solution to this problem is to use parentheses to explicitly group related

operators. Take a look at the following SELECT statement and output:

Input

Click here to view code image

SELECT prod_name, prod_price

FROM products

WHERE (vend_id = 1002 OR vend_id = 1003) AND prod_price >= 10;

Output

prod_name prod_price

---------------- ----------

Detonator 13.00

Bird seed 10.00

Safe 50.00

TNT (5 sticks) 10.00

Analysis

The only difference between this SELECT statement and the earlier one is that,

in this statement, the first two WHERE clause conditions are enclosed within

parentheses. Because parentheses have a higher order of evaluation than either

AND or OR operators, SQL Server first filters the OR condition within those

parentheses. The SQL statement then becomes any products made by either

vendor 1002 or vendor 1003 costing 10 or greater, which is exactly what you

want.

Tip: Using Parentheses in WHERE Clauses

Whenever you write WHERE clauses that use both AND and OR

operators, use parentheses to explicitly group the operators. Don’t

ever rely on the default evaluation order, even if it is exactly what

you want. There is no downside to using parentheses, and you are

always better off eliminating any ambiguity.

Using the IN Operator

Parentheses have another very different use in WHERE clauses. The IN operator

is used to specify a range of conditions, any of which can be matched. IN takes a

comma-delimited list of valid values, all enclosed within parentheses. The

following example demonstrates this:

Input

SELECT prod_name, prod_price

FROM products

WHERE vend_id IN (1002,1003)

ORDER BY prod_name;

Output

prod_name prod_price

---------------- ----------

Bird seed 10.00

Carrots 2.50

Detonator 13.00

Fuses 3.42

Oil can 8.99

Safe 50.00

Sling 4.49

TNT (1 stick) 2.50

TNT (5 sticks) 10.00

Analysis

The SELECT statement retrieves all products made by vendor 1002 and vendor

1003. The IN operator is followed by a comma-delimited list of valid values,

and the entire list must be enclosed within parentheses.

If you are thinking that the IN operator accomplishes the same goal as OR, you

are right. The following SQL statement accomplishes the exact same thing as the

previous example:

Input

Click here to view code image

SELECT prod_name, prod_price

FROM products

WHERE vend_id = 1002 OR vend_id = 1003

ORDER BY prod_name;

Output

prod_name prod_price

---------------- ----------

Bird seed 10.00

Carrots 2.50

Detonator 13.00

Fuses 3.42

Oil can 8.99

Safe 50.00

Sling 4.49

TNT (1 stick) 2.50

TNT (5 sticks) 10.00

Why use the IN operator? Here are the advantages:

When you are working with long lists of valid options, the IN operator

syntax is far cleaner and easier to read.

The order of evaluation is easier to manage when IN is used (because

fewer operators are used).

IN operators almost always execute more quickly than lists of OR

operators.

The biggest advantage of IN is that the IN operator can contain another

SELECT statement, enabling you to build highly dynamic WHERE clauses.

You’ll look at this in detail in Lesson 13, “Working with Subqueries.”

New Term: IN

A keyword used in a WHERE clause to specify a list of values to be

matched using an OR comparison.

Using the NOT Operator

The WHERE clause’s NOT operator has one function and one function only

—NOT negates whatever condition comes next.

New Term: NOT

A keyword used in a WHERE clause to negate a condition.

The following example demonstrates the use of NOT. To list the products made

by all vendors except vendors 1002 and 1003, you can use the following:

Input

Click here to view code image

SELECT prod_name, prod_price

FROM products

WHERE vend_id NOT IN (1002,1003)

ORDER BY prod_name;

Output

prod_name prod_price

--------------- ----------

.5 ton anvil 5.99

1 ton anvil 9.99

2 ton anvil 14.99

JetPack 1000 35.00

JetPack 2000 55.00

Analysis

The NOT here negates the condition that follows it; so instead of matching

vend_id to 1002 or 1003, SQL Server matches vend_id to anything that is

not 1002 or 1003.

So why use NOT? Well, for simple WHERE clauses, there really is no advantage

to using NOT. NOT is useful in more complex clauses. For example, using NOT

in conjunction with an IN operator makes it simple to find all rows that do not

match a list of criteria.

Summary

This lesson picked up where the last lesson left off and taught you how to

combine WHERE clauses with the AND and OR operators. You also learned how

to explicitly manage the order of evaluation, and how to use the IN and NOT

operators.

Lesson 8. Using Wildcard Filtering

In this lesson, you learn what wildcards are, how they are used, and how to

perform wildcard searches using the LIKE operator for sophisticated filtering of

retrieved data.

Using the LIKE Operator

All the previous operators we studied filter against known values. Be it matching

one or more values, testing for greater-than or less-than known values, or

checking a range of values, the common denominator is that the values used in

the filtering are known. But filtering data that way does not always work. For

example, how could you search for all products that contain the text anvil within

the product name? That cannot be done with simple comparison operators; that’s

a job for wildcard searching. Using wildcards, you can create search patterns that

can be compared against your data. In this example, if you want to find all

products that contain the words anvil, you could construct a wildcard search

pattern enabling you to find that anvil text anywhere within a product name.

New Term: Wildcards

Special characters used to match parts of a value.

New Term: Search pattern

A search condition made up of literal text, wildcard characters, or

any combination of the two.

The wildcards themselves are actually characters that have special meanings

within SQL WHERE clauses, and SQL supports several wildcard types.

To use wildcards in search clauses, you must use the LIKE operator. LIKE

instructs SQL Server that the following search pattern is to be compared using a

wildcard match rather than a straight equality match.

Note: Predicates

When is an operator not an operator? When it is a predicate.

Technically, LIKE is a predicate, not an operator. The end result is

the same; just be aware of this term in case you run across it in the

SQL Server documentation.

The Percent Sign (%) Wildcard

The most frequently used wildcard is the percent sign (%). Within a search string,

% means match any number of occurrences of any character. For example, to

find all products that start with the word jet, you can issue the following

SELECT statement:

Input

SELECT prod_id, prod_name

FROM products

WHERE prod_name LIKE 'jet%';

Output

prod_id prod_name

---------- ---------------

JP1000 JetPack 1000

JP2000 JetPack 2000

Analysis

This example uses a search pattern of 'jet%'. When this clause is evaluated,

any value that starts with jet is retrieved. The % tells SQL Server to accept any

characters after the word jet, regardless of how many characters there are.

Note: Case sensitivity

Depending on how SQL Server is configured, searches might be

case sensitive, in which case 'jet%' would not match JetPack

1000. But the default behavior on most SQL Server installations is

case insensitive.

Wildcards can be used anywhere within the search pattern, and multiple

wildcards can be used as well. The following example uses two wildcards, one at

either end of the pattern:

Input

Click here to view code image

SELECT prod_id, prod_name

FROM products

WHERE prod_name LIKE '%anvil%';

Output

prod_id prod_name

---------- --------------

ANV01 .5 ton anvil

ANV02 1 ton anvil

ANV03 2 ton anvil

Analysis

The search pattern '%anvil%' means match any value that contains the text

anvil anywhere within it, regardless of any characters before or after that text.

Wildcards can also be used in the middle of a search pattern, although that is

rarely useful.

It is important to note that, in addition to matching one or more characters, %

also matches zero characters. % represents zero, one, or more characters at the

specified location in the search pattern.

Note: Watch for Trailing Spaces

Trailing spaces can interfere with wildcard matching. For example,

if any of the instances of anvil had been saved with one or more

spaces after the word, the clause WHERE prod_name LIKE

'%anvil' would not have matched them because of the additional

characters after the final l. One simple solution to this problem is to

always append a final % to the search pattern, as we did here. A

better solution is to trim the spaces using functions, as discussed in

Lesson 10, “Using Data Manipulation Functions.”

Caution: Watch for NULL

Although it may seem that the % wildcard matches anything, there is

one exception, NULL. Not even the clause WHERE prod_name

LIKE '%' will match a row with the value NULL as the product

name.

The Underscore (_) Wildcard

Another useful wildcard is the underscore (_). The underscore is used just like %,

but instead of matching multiple characters, the underscore matches just a single

character.

Take a look at this example:

Input

Click here to view code image

SELECT prod_id, prod_name

FROM products

WHERE prod_name LIKE '_ ton anvil%';

Output

prod_id prod_name

---------- --------------

ANV02 1 ton anvil

ANV03 2 ton anvil

Analysis

The search pattern used in this WHERE clause specifies a wildcard followed by

literal text. The results shown are the only rows that match the search pattern:

The underscore matches 1 in the first row and 2 in the second row. The .5 ton

anvil product did not match because the search pattern matched a single

character, not two. By contrast, the following SELECT statement uses the %

wildcard and returns three matching products:

Input

Click here to view code image

SELECT prod_id, prod_name

FROM products

WHERE prod_name LIKE '% ton anvil%';

Output

prod_id prod_name

---------- --------------

ANV01 .5 ton anvil

ANV02 1 ton anvil

ANV03 2 ton anvil

Unlike %, which can match zero characters, _ always matches one character—no

more and no less.

The Brackets ([]) Wildcard

The brackets ([]) wildcard is used to specify a set of characters, any one of

which must match a character in the specified position (the location of the

wildcard).

For example, to find all contacts whose names begin with the letter E or the

letter J, you can do the following:

Input

Click here to view code image

SELECT cust_contact

FROM customers

WHERE cust_contact LIKE '[EJ]%'

ORDER BY cust_contact;

Output

cust_contact

----------------

E Fudd

Jerry Mouse

Jim Jones

Analysis

The WHERE clause in this statement is '[EJ]%'. This search pattern uses two

different wildcards. The [EJ] matches any contact name that begins with either

of the letters within the brackets, and it also matches only a single character.

Therefore, any names longer than one character do not match. The % wildcard

after the [EJ] matches any number of characters after the first character, thus

returning the desired results.

This wildcard can be negated by prefixing the characters with ^ (the carat

character). For example, the following matches any contact name that does not

begin with the letter E or the letter J (the opposite of the previous example):

Input

Click here to view code image

SELECT cust_contact

FROM customers

WHERE cust_contact LIKE '[^EJ]%'

ORDER BY cust_contact;

Of course, you can accomplish the same result using the NOT operator. The only

advantage of ^ is that it can simplify the syntax if you are using multiple WHERE

clauses:

Input

Click here to view code image

SELECT cust_contact

FROM Customers

WHERE NOT cust_contact LIKE '[EJ]%'

ORDER BY cust_contact;

Tips for Using Wildcards

As you can see, T-SQL’s wildcards are extremely powerful. But that power

comes with a price: Wildcard searches typically take far longer to process than

any other search types discussed previously. Here are some tips to keep in mind

when using wildcards:

Don’t overuse wildcards. If another search operator will do, use it instead.

When you do use wildcards, try to not use them at the beginning of the

search pattern unless absolutely necessary. Search patterns that begin with

wildcards are the slowest to process.

Pay careful attention to the placement of the wildcard symbols. If they are

misplaced, you might not return the data you intended.

Having said that, wildcards are an important and useful search tool, and one that

you will use frequently.

Summary

In this lesson, you learned what wildcards are and how to use SQL wildcards

within your WHERE clauses. You also learned that wildcards should be used

carefully and never overused.

Lesson 9. Creating Calculated Fields

In this lesson, you learn what calculated fields are, how to create them, and how

to use aliases to refer to them from within your application.

Understanding Calculated Fields

Data stored within a database’s tables is often not available in the exact format

needed by your applications. Here are some examples:

You need to display a field containing the name of a company along with

the company’s location, but that information is stored in separated table

columns.

City, state, and ZIP Code are stored in separate columns (as they should

be), but your mailing label printing program needs them retrieved as one

correctly formatted field.

Column data is in mixed upper- and lowercase, and your report needs all

data presented in uppercase.

An order items table stores item price and quantity but not the expanded

price (price multiplied by quantity) of each item. To print invoices, you

need that expanded price.

You need total, averages, or other calculations based on table data.

In each of these examples, the data stored in the table is not exactly what your

application needs. Rather than retrieve the data as it is and then reformat it

within your client application or report, what you really want is to retrieve

converted, calculated, or reformatted data directly from the database.

This is where calculated fields come in. Unlike all the columns we retrieved in

the lessons thus far, calculated fields don’t actually exist in database tables.

Rather, a calculated field is created on the fly within a SQL SELECT statement.

New Term: Field

Essentially means the same thing as column and often is used

interchangeably, although database columns are typically called

columns and the term fields is normally used in conjunction with

calculated fields.

It is important to note that only the database knows which columns in a SELECT

statement are actual table columns and which are calculated fields. From the

perspective of a client (for example, your application), a calculated field’s data is

returned in the same way as data from any other column.

Tip: Client versus Server Formatting

Many of the conversions and reformatting that can be performed

within SQL statements can also be performed directly in your client

application. However, as a rule, it is far quicker to perform these

operations on the database server than it is to perform them within

the client because database management systems (DBMSs) are built

to perform this type of processing quickly and efficiently.

Concatenating Fields

To demonstrate working with calculated fields, let’s start with a simple example

—creating a title made up of two columns.

The vendors table contains vendor name and address information. Imagine

you are generating a vendor report and need to list the vendor location as part of

the vendor name in the format name (location).

The report wants a single value, and the data in the table is stored in two

columns: vend_name and vend_country. In addition, you need to surround

vend_country with parentheses, and those are definitely not stored in the

database table. The SELECT statement that returns the vendor names and

locations is simple enough, but how would you create this combined value?

New Term: Concatenate

Joining values together (by appending them to each other) to form a

single long value.

The solution is to concatenate the two columns. In T-SQL SELECT statements,

you can concatenate columns using the + operator.

Input

Click here to view code image

SELECT vend_name + ' (' + vend_country + ')'

FROM vendors

ORDER BY vend_name;

Output

Click here to view code image

--------------------------------------------------

ACME (USA )

Anvils R Us (USA )

Furball Inc. (USA )

Jet Set (England )

Jouets Et Ours (France )

LT Supplies (USA )

Note: Incomplete Results

If you are not seeing the full width of the results, you may need to

resize the columns in the Results grid by dragging column edges.

Analysis

The + operator concatenates strings, appending them to each other to create one

bigger string. The previous SELECT statements concatenate four elements:

The name stored in the vend_name column

A string containing a space and an opening parenthesis

The country stored in the vend_country column

A string containing the closing parenthesis

As you can see in the output shown previously, the SELECT statement returns a

single column (a calculated field) containing all four of these elements as one

unit. However, because of how SQL Server stores data in fixed-length columns,

the retrieved columns were all padded with spaces to their maximum length. As

such, the new calculated field contains extraneous spaces and is not exactly what

we were looking for.

Back in Lesson 8, “Using Wildcard Filtering,” I mentioned the need to trim data

so as to remove any trailing spaces. This can be done using the T-SQL

RTrim() function (or rtrim() or RTRIM(), you get the idea), as follows:

Input

Click here to view code image

SELECT RTrim(vend_name) + ' (' + RTrim(vend_country) + ')'

FROM vendors

ORDER BY vend_name;

Output

Click here to view code image

--------------------------------------------------

ACME (USA)

Anvils R Us (USA)

Furball Inc. (USA)

Jet Set (England)

Jouets Et Ours (France)

LT Supplies (USA)

Analysis

The RTrim() function trims all spaces from the right of a value. By using

RTrim(), you can trim the individual columns properly.

Note: The LTrim() Function

In addition to RTrim() (which, as you’ve just seen, trims the right

side of a string), T-SQL also supports the use of LTrim() (which

trims the left side of a string). To trim both the right and left sides of

a string, use both functions, as in

RTrim(LTrim(vend_name)).

Using Aliases

The SELECT statement used to concatenate the address field works well, as

shown in the previous output. But what is the name of this new calculated

column? Well, the truth is, it has no name; it is simply a value. Although this can

be fine if you are just looking at the results in a SQL query tool, an unnamed

column cannot be used within a client application because the client has no way

to refer to that column.

To solve this problem, SQL supports column aliases. An alias is just that, an

alternative name for a field or value. Aliases are assigned with the AS keyword.

Take a look at the following SELECT statement:

Input

Click here to view code image

SELECT RTrim(vend_name) + ' ('+ RTrim(vend_country) + ')' AS

vend_title

FROM vendors

ORDER BY vend_name;

Output

Click here to view code image

vend_title

--------------------------------------------------

ACME (USA)

Anvils R Us (USA)

Furball Inc. (USA)

Jet Set (England)

Jouets Et Ours (France)

LT Supplies (USA)

Analysis

The SELECT statement itself is the same as the one used in the previous code

snippet, except that here the calculated field is followed by the text AS

vend_title. This instructs SQL Server to create a calculated field named

vend_title containing the results of the specified calculation. As you can see

in the output, the results are the same as before, but the column is now named

vend_title and any client application can refer to this column by name, just

as it would any actual table column.

Tip: AS Is Optional

Unlike in most other SQL implementations, in T-SQL the AS

keyword is actually optional. As such, SELECT vend_name AS

VendName and SELECT vend_nameVendName accomplish the

same thing. In practice, it is a good idea to always specify the AS

keyword (so that you’ll be used to using it when you find yourself

using another DBMS).

Note: Derived Columns

Aliases are also sometimes referred to as derived columns, so

regardless of the term you run across, the meaning is the same.

Aliases have other uses, too. Some common uses include renaming a column if

the real table column name contains illegal characters (for example, spaces) and

expanding column names if the original names are either ambiguous or easily

misread. For example, if a table contains a column named Last Name (with a

space in the column name), you’d have a very hard time using that column in

SQL statements and in any applications. The solution would be to use an alias,

like this:

Click here to view code image

SELECT [Last Name] AS LastName

Here, [and] are used to delimit the column name, and Last Name would then

be aliased as LastName.

Performing Mathematical Calculations

Another frequent use for calculated fields is performing mathematical

calculations on retrieved data. Let’s take a look at an example. The orders

table contains all orders received, and the orderitems table contains the

individual items within each order. The following SQL statement retrieves all the

items in order number 20005:

Input

Click here to view code image

SELECT prod_id, quantity, item_price

FROM orderitems

WHERE order_num = 20005;

Output

Click here to view code image

prod_id quantity item_price

---------- ----------- ---------------------

ANV01 10 5.99

ANV02 3 9.99

TNT2 5 10.00

FB 1 10.00

The item_price column contains the per-unit price for each item in an order.

To expand the item price (item price multiplied by quantity ordered), you simply

do the following:

Input

Click here to view code image

SELECT prod_id,

quantity,

item_price,

quantity*item_price AS expanded_price

FROM orderitems

WHERE order_num = 20005;

Output

Click here to view code image

prod_id quantity item_price expanded_price

---------- ----------- --------------------- --------------

ANV01 10 5.99 59.90

ANV02 3 9.99 29.97

TNT2 5 10.00 50.00

FB 1 10.00 10.00

Analysis

The expanded_price column shown in the previous output is a calculated

field; the calculation is simply quantity*item_price. The client

application can now use this new calculated column just as it would any other

column.

T-SQL supports the basic mathematical operators listed in Table 9.1. In addition,

parentheses can be used to establish order of evaluation (also referred to as

precedence). Refer to Lesson 7, “Advanced Data Filtering,” for an explanation

of precedence.

TABLE 9.1 T-SQL Mathematical Operators

Tip: How to Test Calculations

SELECT provides a great way to test and experiment with functions

and calculations. Although SELECT is usually used to retrieve data

from a table, the FROM clause may be omitted to simply access and

work with expressions. For example, SELECT 3 * 2; would

return 6, SELECT RTrim(LTrim(' abc ')); would return

abc, and SELECT GetDate() uses the GetDate() function to

return the current date and time. You get the idea—use SELECT to

experiment as needed.

Summary

In this lesson, you learned what calculated fields are and how to create them. We

used examples demonstrating the use of calculated fields for both string

concatenation and mathematical operations. In addition, you learned how to

create and use aliases so your application can refer to calculated fields.

Lesson 10. Using Data Manipulation

Functions

In this lesson, you learn what functions are, what types of functions T-SQL

supports, and how to use these functions.

Understanding Functions

Like almost any other computer language, SQL supports the use of functions to

manipulate data. Functions are operations that are usually performed on data,

usually to facilitate conversion and manipulation.

An example of a function is RTrim(), which we used in the last lesson to trim

any spaces from the end of a string.

Note: Functions Are Less Portable Than SQL

Code that runs on multiple systems is said to be portable. Most SQL

statements are relatively portable, and when differences between

SQL implementations do occur they are usually not that difficult to

deal with. Functions, on the other hand, tend to be far less portable.

Just about every major database management system (DBMS)

supports functions that others don’t, and sometimes the differences

are significant.

With code portability in mind, some SQL programmers shy away

from using implementation-specific features. Although this is a

somewhat noble and idealistic view, it is not always in the best

interests of application performance. If you opt not to use these

functions, you make your application code work harder. It must use

other methods to do what the DBMS could have done more

efficiently.

If you do decide to use functions, make sure you comment your

code well, so that at a later date you (or another developer) will

know exactly to which SQL implementation you were writing.

Using Functions

Most SQL implementations support the following types of functions:

Text functions are used to manipulate strings of text (for example,

trimming or padding values and converting values to upper- and

lowercase).

Numeric functions are used to perform mathematical operations on

numeric data (for example, returning absolute numbers and performing

algebraic calculations).

Date and time functions are used to manipulate date and time values and to

extract specific components from these values (for example, returning

differences between dates and checking date validity).

System functions return information specific to the DBMS being used (for

example, returning user login information or checking version specifics).

Text Manipulation Functions

You’ve already seen an example of text manipulation functions in the last lesson

—the RTrim() function was used to trim white space from the end of a column

value. Here is another example, this time using the Upper() function:

Input

Click here to view code image

SELECT vend_name, Upper(vend_name) AS vend_name_upcase

FROM vendors

ORDER BY vend_name;

Output

Click here to view code image

vend_name vend_name_upcase

--------------------- ----------------

ACME ACME

Anvils R Us ANVILS R US

Furball Inc. FURBALL INC.

Jet Set JET SET

Jouets Et Ours JOUETS ET OURS

LT Supplies LT SUPPLIES

Analysis

As you can see, Upper() converts text to uppercase, and so in this example

each vendor is listed twice—first exactly as stored in the vendors table and

then converted to uppercase in column vend_name_upcase.

Table 10.1 lists some commonly used text manipulation functions.

TABLE 10.1 Commonly Used Text Manipulation Functions

One item in Table 10.1 requires further explanation. SOUNDEX is an algorithm

that converts any string of text into an alphanumeric pattern describing the

phonetic representation of that text. SOUNDEX takes into account similar-

sounding characters and syllables, enabling strings to be compared by how they

sound rather than how they have been typed. Although SOUNDEX is not a SQL

concept, T-SQL (like many other DBMSs) offers SOUNDEX support.

Here’s an example using the Soundex() function. Customer Coyote Inc.

is in the customers table and has a contact named Y. Lee. But what if that

were a typo, and the contact actually was supposed to have been Y. Lie?

Obviously, searching by the correct contact name would return no data, as shown

here:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers

WHERE cust_contact ='Y. Lie';

Output

Click here to view code image

cust_name cust_contact

----------------------- -------------------

Now try the same search using the Soundex() function to match all contact

names that sound similar to Y. Lie:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers

WHERE Soundex(cust_contact) = Soundex('Y Lie');

Output

Click here to view code image

cust_name cust_contact

----------------------- -------------------

Coyote Inc. Y Lee

Analysis

In this example, the WHERE clause uses the Soundex() function to convert

both the cust_contact column value and the search string to their

SOUNDEX values. Because Y. Lee and Y. Lie sound alike, their

SOUNDEX values match, and so the WHERE clause correctly filtered the desired

data.

Date and Time Manipulation Functions

Dates and times are stored in tables using special datatypes with special internal

formats so they may be sorted or filtered quickly and efficiently, as well as to

save physical storage space.

The format used to store dates and times is usually of no use to your

applications; therefore, date and time functions are almost always used to read,

expand, and manipulate these values. Because of this, date and time

manipulation functions are some of the most important functions in the T-SQL

language.

Table 10.2 lists some commonly used date and time manipulation functions.

TABLE 10.2 Commonly Used Date and Time Manipulation Functions

Functions DateDiff(), DateName(), and DatePart() require that a date

part identifier be passed to them. Table 10.3 lists the supported date parts (you

may specify the date part or an abbreviation).

TABLE 10.3 Supported Date Parts and Abbreviations

For example, to obtain the days of the week that orders were placed, you could

use DatePart() specifying weekday as the part:

Input

Click here to view code image

SELECT order_num,

DatePart(weekday, order_date) AS weekday

FROM orders;

Output

order_num weekday

----------- -----------

20005 5

20006 2

20007 6

20008 2

20009 7

Analysis

DatePart() extracts specified date parts from a date.

DatePart(weekday, order_date) returns the day of the week for each

order_date value, as shown in the aliases weekday column.

To return named weekdays (instead of numbers), you can use the DateName()

function in much the same way:

Input

Click here to view code image

SELECT order_num,

DateName(weekday, order_date) AS weekday

FROM orders;

Output

order_num weekday

----------- -------------

20005 Saturday

20006 Wednesday

20007 Sunday

20008 Wednesday

20009 Monday

Analysis

Like DatePart(), DateName() accepts a date part as its first parameter.

DateName(weekday, 2) returns Monday. DateName(month, 8)

returns August.

These functions can be used with WHERE to filter data, too:

Input

Click here to view code image

SELECT order_num

FROM orders

WHERE DatePart(month, order_date) = 10;

Output

order_num

20008

20009

Analysis

Here DatePart() is used to obtain just the month from order_date. By

comparing this to 10, only orders placed in October (month 10) are retrieved.

Tip: DatePart() Shortcuts

Day(), Month(), and Year() are shortcuts for

DatePart(day,), DatePart(month,), and

DatePart(year,), respectively. So, the prior example, which

retrieves orders placed in October:

Click here to view code image

SELECT order_num

FROM orders

WHERE DatePart(month, order_date) = 10;

could be shortened to:

SELECT order_num

FROM orders

WHERE Month(order_date) = 10;

When filtering using dates it is important to keep in mind the date format used

by SQL Server. Whenever you specify a date—be it inserting or updating table

values, or filtering using WHERE clauses—the date should be in one of the

recognized formats. T-SQL supports several string representations of dates,

including:

2016-08-17

August 17, 2016

20160817

8/17/16

Of these formats, the last should be avoided (after all, is 04/05/16 May 4th of

2016, April 5th of 2016, May 16th of 2004, or...? You get the idea).

Tip: Always Use Four-Digit Years

Two-digit years are supported, and SQL Server treats years 00–49

as 2000–2049 and years 50–99 as 1950–1999. Although these

might in fact be the intended years, it is far safer to always use a full

four-digit year so that SQL Server does not have to make any

assumptions for you.

As such, a basic date comparison should be simple enough:

Input

Click here to view code image

SELECT cust_id, order_num

FROM orders

WHERE order_date = '2016-09-01';

Output

cust_id order_num

----------- -----------

10001 20005

Analysis

This SELECT statement worked; it retrieved a single order record, one with an

order_date of 2016-09-01.

But is using WHERE order_date = '2016-09-01' safe? order_date

has a datatype of date time. This type stores dates along with time values. The

values in our sample tables all have times of 00:00:00, but that might not

always be the case. What if order dates were stored using the current date and

time (so you’d not only know the order date but also the time of day the order

was placed)? Then WHERE order_date = '2016-09-01' fails if, for

example, the stored order_date value is 2016-09-01 11:30:05. Even

though a row with that date is present, it is not retrieved because the WHERE

match failed.

The solution is to instruct SQL Server to compare the specified date only to the

date portion of the column instead of the entire column value. To do this, you

can use the DateDiff() function. DateDiff() is used to determine the

difference between two dates. Look at this example:

Input

Click here to view code image

SELECT cust_id, order_num

FROM orders

WHERE DateDiff(day, order_date,'2016-09-01') = 0;

Output

cust_id order_num

----------- -----------

10001 20005

Analysis

Like DatePart() and DateName() used previously, DateDiff() requires

that three parameters be passed to it. The first is the date part to compare. If you

want to compare two dates by day (checking that they are the same day), then

specify day. If you want to check that two dates are the same month (regardless

of day of month), then specify month, and so on. The next two parameters are

the dates to be compared. DateDiff() returns the difference between the two

dates. A return value of 0 means that there is no difference (they match). A

return value of 5 would indicate that the first date is 5 greater than the second

date (5 months or 5 days, and so on, depending on the date part specified).

Similarly, a value of –3 would indicate that the second date is 3 greater than the

first date.

Tip: Always Use DateDiff()

When comparing dates, always use DateDiff(), and don’t make

assumptions about how dates are stored.

The truth is that the above example could also have been written like this:

Input

SELECT cust_id, order_num

FROM orders

WHERE Year(order_date) = 2016

AND Month(order_date) = 9

AND Day(order_date) = 1;

Output

cust_id order_num

----------- -----------

10001 20005

Analysis

Here three functions are being used, Year() extracts the year from

order_date and compares it to 2016, Month() extracts the month and

compares it to 9, and Day() extracts the day and compares is to 1. The results

are the same, as you can see. As is often the case with SQL, there are many ways

to solve any problem, so use whatever works best for you.

Now that you know how to use dates to test for equality, using all the other

operators (introduced in Lesson 6, “Filtering Data”) should be self-explanatory.

But one other type of date comparison warrants explanation. What if you wanted

to retrieve all orders placed in September 2016? A simple equality test does not

work because it matches the day of the month too. There are several solutions,

one of which follows:

Input

Click here to view code image

SELECT cust_id, order_num

FROM orders

WHERE DateDiff(month, order_date, '2016-09-01') = 0;

Output

cust_id order_num

----------- -----------

10001 20005

10003 20006

10004 20007

Analysis

Here, DateDiff() is used to locate any orders that match 2016-09-01,

matching those that are the same month. The truth is, 2016-09-10 or 2016-

09-30 or any other date in September 2016 would have worked just as well.

Because the specified date part is month, dates will match if they are in the

same month (and the day of month is ignored).

Here’s another solution:

Input

Click here to view code image

SELECT cust_id, order_num

FROM orders

WHERE Year(order_date) = 2016 AND Month(order_date) = 9;

Analysis

Year() returns the year from a date (or a date time). Similarly, Month()

returns the month from a date. WHERE Year(order_date) = 2016 AND

Month(order_date) = 9 thus retrieves all rows that have an

order_date in year 2016 and in month 9.

Numeric Manipulation Functions

Numeric manipulation functions do just that—manipulate numeric data. These

functions tend to be used primarily for algebraic, trigonometric, or geometric

calculations and therefore are not as frequently used as string or date and time

manipulation functions.

The ironic thing is that of all the functions found in the major DBMSs, the

numeric functions are the ones that are the most uniform and consistent. Table

10.4 lists some of the more commonly used numeric manipulation functions.

TABLE 10.4 Commonly Used Numeric Manipulation Functions

Summary

In this lesson, you learned how to use SQL’s data manipulation functions, paying

special attention to working with dates.

Lesson 11. Summarizing Data

In this lesson, you learn what the SQL aggregate functions are and how to use

them to summarize table data.

Using Aggregate Functions

It is often necessary to summarize data without actually retrieving it all, and SQL

Server provides special functions for this purpose. Using these functions, T-SQL

queries are often used to retrieve data for analysis and reporting purposes. Here

are some examples of this type of retrieval:

Determining the number of rows in a table (or the number of rows that

meet some condition or contain a specific value)

Obtaining the sum of a group of rows in a table

Finding the highest, lowest, and average values in a table column (either

for all rows or for specific rows)

In each of these examples, you want a summary of the data in a table, not the

actual data itself. Therefore, returning to the actual table data would be a waste

of time and processing resources (not to mention bandwidth). To repeat, all you

really want is the summary information.

To facilitate this type of retrieval, T-SQL features a set of aggregate functions,

some of which are listed in Table 11.1. These functions enable you to perform all

the types of retrieval just enumerated.

TABLE 11.1 SQL Aggregate Functions

New Term: Aggregate Functions

Functions that operate on a set of rows to calculate and return a

single value.

The use of each of these functions is explained in the following sections.

Note: Standard Deviation

A series of standard deviation aggregate functions is also supported

by T-SQL, but these functions are not covered in the lessons.

The Avg() Function

Avg() is used to return the average value of a specific column by counting both

the number of rows in the table and the sum of their values. Avg() can be used

to return the average value of all columns or of specific columns or rows.

This first example uses Avg() to return the average price of all the products in

the products table:

Input

Click here to view code image

SELECT Avg(prod_price) AS avg_price

FROM products;

Output

avg_price

---------------------

16.1335

Analysis

The previous SELECT statement returns a single value, avg_price, which

contains the average price of all products in the products table. avg_price

is an alias, as explained in Lesson 9, “Creating Calculated Fields.”

Avg() can also be used to determine the average value of specific columns or

rows. The following example returns the average price of products offered by a

specific vendor:

Input

Click here to view code image

SELECT Avg(prod_price) AS avg_price

FROM products

WHERE vend_id = 1003;

Output

avg_price

---------------------

13.2128

Analysis

This SELECT statement differs from the previous one only in that this one

contains a WHERE clause. The WHERE clause filters only products with a

vend_id of 1003; therefore, the value returned in avg_price is the average

of just that vendor’s products.

Caution: Individual Columns Only

Avg() may only be used to determine the average of a specific

numeric column, and that column name must be specified as the

function parameter. To obtain the average value of multiple

columns, you must use multiple Avg() functions.

Note: NULL Values

Column rows containing NULL values are ignored by the Avg()

function.

The Count() Function

Count() does just that: It counts. Using Count(), you can determine the

number of rows in a table or the number of rows that match a specific criterion.

Count() can be used in two ways:

Use Count(*) to count the number of rows in a table, regardless of

whether columns contain values or NULL values.

Use Count(column) to count the number of rows that have values in a

specific column, ignoring NULL values.

This first example returns the total number of customers in the customers

table:

Input

SELECT Count(*) AS num_cust

FROM customers;

Output

num_cust

-----------

Analysis

In this example, Count(*) is used to count all rows, regardless of values. The

count is returned in num_cust.

The following example counts just the customers with an email address:

Input

Click here to view code image

SELECT Count(cust_email) AS num_cust

FROM customers;

Output

num_cust

-----------

Analysis

This SELECT statement uses Count(cust_email) to count only rows with

a value in the cust_email column. In this example, cust_email is 3

(meaning that only three of the five customers have an email address).

Note: NULL Values

Column rows with NULL values in them are ignored by the

Count() function if a column name is specified, but not if the

asterisk (*) is used.

The Max() Function

Max() returns the highest value in a specified column. Max() requires that the

column name be specified, as shown here:

Input

Click here to view code image

SELECT Max(prod_price) AS max_price

FROM products;

Output

max_price

---------------------

55.00

Analysis

Here, Max() returns the price of the most expensive item in the products

table.

Tip: Using Max() with Nonnumeric Data

Although Max() is usually used to find the highest numeric or date

value, T-SQL allows it to be used to return the highest value in any

column, including textual columns. When used with textual data,

Max() returns the row that would be the last if the data were sorted

by that column.

Note: NULL Values

Column rows with NULL values in them are ignored by the Max()

function.

The Min() Function

Min() does the exact opposite of Max(); it returns the lowest value in a

specified column. Like Max(), Min() requires that the column name be

specified, as shown here:

Input

Click here to view code image

SELECT Min(prod_price) AS min_price

FROM products;

Output

min_price

---------------------

2.50

Analysis

Here, Min() returns the price of the least-expensive item in the products

table.

Tip: Using Min() with Nonnumeric Data

Similar to the Max() function, T-SQL allows Min() to be used to

return the lowest value in any columns, including textual columns.

When used with textual data, Min() returns the row that would be

first if the data were sorted by that column.

Note: NULL Values

Column rows with NULL values in them are ignored by the Min()

function.

The Sum() Function

Sum() is used to return the sum (total) of the values in a specific column.

Here is an example to demonstrate this. The orderitems table contains the

actual items in an order, and each item has an associated quantity. The total

number of items ordered (the sum of all the quantity values) can be retrieved

as follows:

Input

Click here to view code image

SELECT Sum(quantity) AS items_ordered

FROM orderitems

WHERE order_num = 20005;

Output

items_ordered

-------------

Analysis

The function Sum(quantity) returns the sum of all the item quantities in an

order, and the WHERE clause ensures that just the right order items are included.

Sum() can also be used to total calculated values. In this next example, the total

order amount is retrieved by totaling item_price*quantity for each item:

Input

Click here to view code image

SELECT Sum(item_price*quantity) AS total_price

FROM orderitems

WHERE order_num = 20005;

Output

total_price

---------------------

149.87

Analysis

The function Sum(item_price*quantity) returns the sum of all the

expanded prices in an order, and again the WHERE clause ensures that just the

correct order items are included.

Tip: Performing Calculations on Multiple Columns

All the aggregate functions can be used to perform calculations on

multiple columns using the standard mathematical operators, as

shown in the example.

Note: NULL Values

Column rows with NULL values in them are ignored by the Sum()

function.

Aggregates on Distinct Values

The five aggregate functions can all be used in two ways:

To perform calculations on all rows, you specify the ALL argument or

specify no argument at all (because ALL is the default behavior).

To include only unique values, you specify the DISTINCT argument.

Tip: ALL is the Default

The ALL argument need not be specified because it is the default

behavior. If DISTINCT is not specified, ALL is assumed.

The following example uses the Avg() function to return the average product

price offered by a specific vendor. It is the same SELECT statement used in the

previous example, but here the DISTINCT argument is used so the average

takes into account only unique prices:

Input

Click here to view code image

SELECT Avg(DISTINCT prod_price) AS avg_price

FROM products

WHERE vend_id = 1003;

Output

avg_price

---------------------

15.998

Analysis

As you can see, in this example avg_price is higher when DISTINCT is used

because there are multiple items with the same lower price. Excluding them

raises the average price.

Caution

DISTINCT may be used with Count() only if a column name is

specified. DISTINCT may not be used with Count(*).

Therefore, Count(DISTINCT *) is not allowed and generates an

error. Similarly, DISTINCT must be used with a column name and

not with a calculation or expression.

Tip: Using DISTINCT with Min() and Max()

Although DISTINCT can technically be used with Min() and

Max(), there is actually no value in doing so. The minimum and

maximum values in a column are the same whether or not only

distinct values are included.

Combining Aggregate Functions

All the examples of aggregate functions used thus far have involved a single

function. But actually, SELECT statements may contain as few or as many

aggregate functions as needed. Look at this example:

Input

Click here to view code image

SELECT Count(*) AS num_items,

Min(prod_price) AS price_min,

Max(prod_price) AS price_max,

Avg(prod_price) AS price_avg

FROM products;

Output

Click here to view code image

num_items price_min price_max price_avg

----------- --------------------- --------------------- -------

14 2.50 55.00 16.1335

Analysis

Here, a single SELECT statement performs four aggregate calculations in one

step and returns four values (the number of items in the products table as well

as the highest, lowest, and average product prices).

Tip: Naming Aliases

When specifying alias names to contain the results of an aggregate

function, try not to use the name of an actual column in the table.

Although there is nothing actually illegal about doing so, using

unique names makes your SQL easier to understand and work with

(and troubleshoot in the future).

Summary

Aggregate functions are used to summarize data. SQL Server supports a range of

aggregate functions, all of which can be used in multiple ways to return just the

results you need. These functions are designed to be highly efficient, and they

usually return results far more quickly than you could calculate yourself within

your own client application.

Lesson 12. Grouping Data

In this lesson, you learn how to group data so you can summarize subsets of

table contents. This involves two new SELECT statement clauses: the GROUP

BY clause and the HAVING clause.

Understanding Data Grouping

In the last lesson, you learned that the SQL aggregate functions can be used to

summarize data. This enables you to count rows, calculate sums and averages,

and obtain high and low values without having to retrieve all the data.

All the calculations thus far were performed on all the data in a table or on data

that matched a specific WHERE clause. As a reminder, the following example

returns the number of products offered by vendor 1003:

Input

SELECT Count(*) AS num_prods

FROM products

WHERE vend_id = 1003;

Output

num_prods

-----------

But what if you want to return the number of products offered by each vendor?

Or products offered only by vendors who offer a single product, or only those

who offer more than 10 products?

This is where groups come into play. Grouping enables you to divide data into

logical sets so you can perform aggregate calculations on each group.

Creating Groups

Groups are created using the GROUP BY clause in your SELECT statement. The

best way to understand this is to look at an example:

Input

Click here to view code image

SELECT vend_id, Count(*) AS num_prods

FROM products

GROUP BY vend_id;

Output

vend_id num_prods

----------- -----------

1001 3

1002 2

1003 7

1005 2

Analysis

The preceding SELECT statement specifies two columns: vend_id, which

contains the ID of a product’s vendor, and num_prods, which is a calculated

field (created using the Count(*) function). The GROUP BY clause instructs

SQL Server to sort the data and group it by vend_id. This causes num_prods

to be calculated once per vend_id rather than once for the entire table. As you

can see in the output, vendor 1001 has three products listed, vendor 1002 has

two products listed, vendor 1003 has seven products listed, and vendor 1005

has two products listed.

Because you used GROUP BY, you did not have to specify each group to be

evaluated and calculated. That was done automatically. The GROUP BY clause

instructs SQL Server to group the data and then perform the aggregate on each

group rather than on the entire result set.

Before you use GROUP BY, here are some important rules about its use that you

need to know:

GROUP BY clauses can contain as many columns as you want. This

enables you to nest groups, providing you with more granular control over

how data is grouped.

If you have multiple groups specified in your GROUP BY clause, data is

summarized at the last specified group. In other words, all the columns

specified are evaluated together when grouping is established (so you

won’t get data back for each individual column level).

Every column listed in GROUP BY must be a retrieved column or a valid

expression (but not an aggregate function). If an expression is used in the

SELECT statement, that same expression must be specified in GROUP BY.

Aliases cannot be used.

Aside from the aggregate calculations statements, every column in your

SELECT statement should be present in the GROUP BY clause.

If the grouping column contains a row with a NULL value, NULL will be

returned as a group. If there are multiple rows with NULL values, they’ll

all be grouped together.

The GROUP BY clause must come after any WHERE clause and before any

ORDER BY clause.

Filtering Groups

In addition to being able to group data using GROUP BY, SQL Server also

allows you to filter which groups to include and which to exclude. For example,

you might want a list of all customers who have made at least two orders. To

obtain this data, you must filter based on the complete group, not on individual

rows.

You’ve already seen the WHERE clause in action (introduced back in Lesson 6,

“Filtering Data.”) But WHERE does not work here because WHERE filters specific

rows, not groups. As a matter of fact, WHERE has no idea what a group is.

So what do you use instead of WHERE? T-SQL provides yet another clause for

this purpose: the HAVING clause. HAVING is very similar to WHERE. In fact, all

types of WHERE clauses you learned about thus far can also be used with

HAVING. The only difference is that WHERE filters rows and HAVING filters

groups.

Tip: HAVING Supports All of WHERE’s Operators

In Lesson 6, “Filtering Data,” and Lesson 7, “Advanced Data

Filtering,” you learned about WHERE clause conditions (including

wildcard conditions and clauses with multiple operators). All the

techniques and options you learned about WHERE can be applied to

HAVING. The syntax is identical; just the keyword is different.

In fact, HAVING is so like WHERE that if you were to use HAVING

without specifying a GROUP BY clause, it would actually function

as a WHERE clause.

So how do you filter groups? Look at the following example:

Input

Click here to view code image

SELECT cust_id, Count(*) AS orders

FROM orders

GROUP BY cust_id

HAVING Count(*) >= 2;

Output

cust_id orders

----------- -----------

10001 2

Analysis

The first three lines of this SELECT statement are similar to the statements

shown previously. The final line adds a HAVING clause that filters on those

groups with a Count(*) >= 2—that is, two or more orders.

As you can see, a WHERE clause does not work here because the filtering is

based on the group aggregate value, not on the values of specific rows.

Note: The Difference Between HAVING and WHERE

Here’s another way to look at it: WHERE filters before data is

grouped, and HAVING filters after data is grouped. This is an

important distinction; rows that are eliminated by a WHERE clause

are not included in the group. This could change the calculated

values, which in turn could affect which groups are filtered based on

the use of those values in the HAVING clause.

So is there ever a need to use both the WHERE and HAVING clauses in one

statement? Actually, yes, there is. Suppose you want to further filter the previous

statement so it returns any customers who placed two or more orders in the past

12 months. To do that, you can add a WHERE clause that filters out just the

orders placed in the past 12 months. You then add a HAVING clause to filter just

the groups with two or more rows in them.

To better demonstrate this, look at the following example, which lists all vendors

who have two or more products priced at 10 or more:

Input

Click here to view code image

SELECT vend_id, Count(*) AS num_prods

FROM products

WHERE prod_price >= 10

GROUP BY vend_id

HAVING Count(*) >= 2;

Output

vend_id num_prods

----------- -----------

1003 4

1005 2

Analysis

This statement warrants an explanation. The first line is a basic SELECT using

an aggregate function—much like the examples thus far. The WHERE clause

filters all rows with a prod_price of at least 10. Data is then grouped by

vend_id, and then a HAVING clause filters just those groups with a count of 2

or more. Without the WHERE clause, two extra rows would have been retrieved

(vendor 1002, who sells only products priced under 10, and vendor 1001, who

sells three products but only one of them is priced greater or equal to 10), as

shown here:

Input

Click here to view code image

SELECT vend_id, Count(*) AS num_prods

FROM products

GROUP BY vend_id

HAVING Count(*) >= 2;

Output

vend_id num_prods

----------- -----------

1001 3

1002 2

1003 7

1005 2

Caution: Not All Datatypes

HAVING and GROUP BY cannot be used with columns of type

text, ntext, and image.

Grouping and Sorting

GROUP BY and ORDER BY perform different but related functions, and many

users confuse the two. To help clarify when and why to use these two clauses,

Table 12.1 summarizes the differences between them.

TABLE 12.1 ORDER BY Versus GROUP BY

The first difference listed in Table 12.1 is extremely important. More often than

not, data grouped using GROUP BY is indeed output in group order. But that is

not always the case, and it is not actually required by the SQL specifications.

Furthermore, you might actually want it sorted differently than it is grouped. Just

because you group data one way (to obtain group-specific aggregate values)

does not mean that you want the output sorted that same way. You should always

provide an explicit ORDER BY clause as well, even if it is identical to the

GROUP BY clause.

Tip: Don’t Forget ORDER BY

As a rule, any time you use a GROUP BY clause, you should also

specify an ORDER BY clause. That is the only way to ensure that

data is sorted properly. Never rely on GROUP BY to sort your data.

To demonstrate the use of both GROUP BY and ORDER BY, let’s look at an

example. The following SELECT statement is similar to the ones shown

previously. It retrieves the order number and total order price of all orders with a

total price of 50 or more:

Input

Click here to view code image

SELECT order_num, Sum(quantity*item_price) AS ordertotal

FROM orderitems

GROUP BY order_num

HAVING Sum(quantity*item_price) >= 50;

Output

Click here to view code image

order_num ordertotal

----------- ---------------------

20005 149.87

20006 55.00

20007 1000.00

20008 125.00

To sort the output by order total, all you need to do is add an ORDER BY clause,

as follows:

Input

Click here to view code image

SELECT order_num, Sum(quantity*item_price) AS ordertotal

FROM orderitems

GROUP BY order_num

HAVING Sum(quantity*item_price) >= 50

ORDER BY ordertotal;

Output

Click here to view code image

order_num ordertotal

----------- ---------------------

20006 55.00

20008 125.00

20005 149.87

20007 1000.00

Analysis

In this example, the GROUP BY clause is used to group the data by order

number (the order_num column) so that the Sum(*) function can return the

total order price. The HAVING clause filters the data so that only orders with a

total price of 50 or more are returned. Finally, the output is sorted using the

ORDER BY clause.

SELECT Clause Ordering

This is probably a good time to review the order in which SELECT statement

clauses are to be specified. Table 12.2 lists all the clauses you have learned thus

far, in the order they must be used.

TABLE 12.2 SELECT Clauses and Their Sequence

Summary

In Lesson 11, “Summarizing Data,” you learned how to use the SQL aggregate

functions to perform summary calculations on your data. In this lesson, you

learned how to use the GROUP BY clause to perform these calculations on

groups of data, returning results for each group. You saw how to use the

HAVING clause to filter specific groups. You also learned the difference between

ORDER BY and GROUP BY and between WHERE and HAVING.

Lesson 13. Working with Subqueries

In this lesson, you learn what subqueries are and how to use them.

Understanding Subqueries

SELECT statements are SQL queries. All the SELECT statements you have seen

thus far are simple queries: single statements retrieving data from individual

database tables.

New Term: Query

Any SQL statement. However, this term is generally used to refer to

SELECT statements.

SQL also enables you to create subqueries—queries that are embedded into

other queries. Why would you want to do this? The best way to understand this

concept is to look at a couple examples.

Filtering by Subquery

The database tables used in all the lessons in this book are relational tables. (See

Appendix B, “The Example Tables,” for a description of each of the tables and

their relationships.) Order data is stored in two tables. The orders table stores

a single row for each order containing an order number, customer ID, and order

date. The individual order items are stored in the related orderitems table.

The orders table does not store customer information. It only stores a

customer ID. The actual customer information is stored in the customers

table.

Now suppose you wanted a list of all the customers who ordered item TNT2.

What would you have to do to retrieve this information? Here are the steps to

accomplish this:

1. Retrieve the order numbers of all orders containing item TNT2.

2. Retrieve the customer ID of all the customers who have orders listed in the

order numbers returned in the previous step.

3. Retrieve the customer information for all the customer IDs returned in the

previous step.

Each of these steps can be executed as a separate query. By doing so, you use the

results returned by one SELECT statement to populate the WHERE clause of the

next SELECT statement.

However, you can also use subqueries to combine all three queries into one

single statement.

The first SELECT statement should be self-explanatory by now. It retrieves the

order_num column for all order items with a prod_id of TNT2. The output

lists the two orders containing this item:

Input

SELECT order_num

FROM orderitems

WHERE prod_id = 'TNT2';

Output

order_num

-----------

20005

20007

The next step is to retrieve the customer IDs associated with orders 20005 and

20007. Using the IN clause, described in Lesson 7, “Advanced Data Filtering,”

you can create a SELECT statement as follows:

Input

Click here to view code image

SELECT cust_id

FROM orders

WHERE order_num IN (20005,20007);

Output

cust_id

-----------

10001

10004

Now, combine the two queries by turning the first (the one that returned the

order numbers) into a subquery. Look at the following SELECT statement:

Input

Click here to view code image

SELECT cust_id

FROM orders

WHERE order_num IN (SELECT order_num

FROM orderitems

WHERE prod_id = 'TNT2');

Output

cust_id

-----------

10001

10004

Analysis

Subqueries are always processed starting with the innermost SELECT statement

and working outward. When the preceding SELECT statement is processed, SQL

Server actually performs two operations.

First, it runs the subquery:

Click here to view code image

SELECT order_num FROM orderitems WHERE prod_id = 'TNT2'

That query returns the two order numbers, 20005 and 20007. Those two

values are then passed to the WHERE clause of the outer query in the comma-

delimited format required by the IN operator. The outer query now becomes

this:

Click here to view code image

SELECT cust_id FROM orders WHERE order_num IN (20005, 20007);

As you can see, the output is correct and exactly the same as the output returned

by the previous hard-coded WHERE clause.

Tip: Formatting Your SQL

SELECT statements containing subqueries can be difficult to read

and debug, especially as they grow in complexity. Breaking up the

queries over multiple lines and indenting the lines appropriately, as

shown here, can greatly simplify working with subqueries.

You now have the IDs of all the customers who ordered item TNT2. The next

step is to retrieve the customer information for each of those customer IDs. Here

is the SQL statement to retrieve the two columns:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers

WHERE cust_id IN (10001,10004);

Input

Instead of hard-coding those customer IDs, you can turn this WHERE clause into

yet another subquery:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers

WHERE cust_id IN (SELECT cust_id

FROM orders

WHERE order_num IN (SELECT order_num

FROM orderitems

WHERE prod_id = 'TNT2'));

Output

Click here to view code image

cust_nam cust_contact

--------------------- ---------------

Coyote Inc. Y Lee

Yosemite Place Y Sam

Analysis

To execute this SELECT statement, SQL Server actually had to perform three

SELECT statements. The innermost subquery returned a list of order numbers

that was then used as the WHERE clause for the subquery above it. That subquery

returned a list of customer IDs that was used as the WHERE clause for the top-

level query. The top-level query actually returned the desired data.

As you can see, using subqueries in a WHERE clause enables you to write

extremely powerful and flexible SQL statements. There is no limit imposed on

the number of subqueries that can be nested, although in practice you will find

that performance tells you when you are nesting too deeply.

Caution: Single Column Only

With the notable exception of subqueries used in conjunction with

EXISTS (as will be shown shortly), subquery SELECT statements

can retrieve only a single column, and attempting to retrieve

multiple columns returns an error.

Although generally used in conjunction with the IN operator, subqueries can

also be used to test for equality (using =), non-equality (using <>), and so on.

Caution: Subqueries and Performance

The code shown here works, and it achieves the desired result.

However, using subqueries is not always the most efficient way to

perform this type of data retrieval, although it might be. More on

this is in Lesson 14, “Joining Tables,” where you will revisit this

same example.

Using Subqueries as Calculated Fields

Another way to use subqueries is in creating calculated fields. Suppose you want

to display the total number of orders placed by every customer in your

customers table. Orders are stored in the orders table along with the

appropriate customer ID.

To perform this operation, follow these steps:

1. Retrieve the list of customers from the customers table.

2. For each customer retrieved, count the number of associated orders in the

orders table.

As you learned in the previous two lessons, you can use SELECT Count(*)

to count rows in a table, and by providing a WHERE clause to filter a specific

customer ID, you can count just that customer’s orders. For example, the

following code counts the number of orders placed by customer 10001:

Input

SELECT Count(*) AS orders

FROM orders

WHERE cust_id = 10001;

To perform that Count(*) calculation for each customer, use Count(*) as a

subquery. Look at the following code:

Input

Click here to view code image

SELECT cust_name,

cust_state,

(SELECT Count(*)

FROM orders

WHERE orders.cust_id = customers.cust_id) AS orders

FROM customers

ORDER BY cust_name;

Output

Click here to view code image

cust_name cust_state orders

--------------------------- ---------- -----------

Coyote Inc. MI 2

E Fudd IL 1

Mouse House OH 0

Wascals IN 1

Yosemite Place AZ 1

Analysis

This SELECT statement returns three columns for every customer in the

customers table: cust_name, cust_state, and orders. orders is a

calculated field that is set by a subquery provided in parentheses. That subquery

is executed once for every customer retrieved. In this example, the subquery is

executed five times because five customers were retrieved.

The WHERE clause in this subquery is a little different from the WHERE clauses

used previously because it uses fully qualified column names (first mentioned in

Lesson 4, “Retrieving Data”). The following clause tells SQL to compare the

cust_id in the orders table to the one currently being retrieved from the

customers table:

Click here to view code image

WHERE orders.cust_id = customers.cust_id

New Term: Correlated Subquery

A subquery that refers to the outer query.

This type of subquery is called a correlated subquery. This syntax—the table

name and the column name separated by a period—must be used whenever there

is possible ambiguity about column names. Why? Well, let’s look at what

happens if fully qualified column names are not used:

Input

Click here to view code image

SELECT cust_name,

cust_state,

(SELECT Count(*)

FROM orders

WHERE cust_id = cust_id) AS orders

FROM customers

ORDER BY cust_name;

Output

Click here to view code image

cust_name cust_state orders

--------------------------- ---------- -----------

Coyote Inc. MI 5

E Fudd IL 5

Mouse House OH 5

Wascals IN 5

Yosemite Place AZ 5

Analysis

Obviously the returned results are incorrect (compare them to the previous

results), but why did this happen? There are two cust_id columns—one in

customers and one in orders—and those two columns need to be compared

to correctly match orders with their appropriate customers. Without fully

qualifying the column names, SQL Server assumes you are comparing the

cust_id in the orders table to itself. As such, the statement

Click here to view code image

SELECT Count(*) FROM orders WHERE cust_id = cust_id;

always returns the total number of orders in the orders table (because SQL

Server checks to see that every order’s cust_id matches itself, which it always

does, of course).

Although subqueries are extremely useful in constructing this type of SELECT

statement, care must be taken to properly qualify ambiguous column names.

Note: Always More Than One Solution

As explained earlier in this lesson, although the sample code shown

here works, it is often not the most efficient way to perform this

type of data retrieval. You will revisit this example in a later lesson.

Checking for Existence with Subqueries

Another use for subqueries is in conjunction with the EXISTS predicate.

EXISTS, when used in a WHERE clause, looks at the results returned by a

subquery—not at specific columns of data, but rather at the number of rows. If

the subquery returns rows, the EXISTS test is true and the WHERE clause

matches. However, if no rows are returned, the EXISTS test is false, and the

WHERE clause does not match.

Look at this SELECT statement:

Input

Click here to view code image

SELECT cust_id, cust_name

FROM customers

WHERE cust_id IN (SELECT cust_id

FROM orders

WHERE DateDiff(month, order_date,

'2016-09-01') = 0

AND customers.cust_id = orders.cust_id);

Output

Click here to view code image

cust_id cust_name

----------- ------------------

10001 Coyote Inc.

10003 Wascals

10004 Yosemite Place

Analysis

This SELECT statement retrieves the customer name and ID for any customers

who made orders in the month of September 2016. Like the examples shown

earlier in this lesson, the WHERE clause uses IN and a subquery to first select the

IDs of customers who made orders the specified month, and then uses the results

of that subquery to select just the desired customers from the customers table.

Now let’s look at another SELECT statement that returns the exact same output:

Input

Click here to view code image

SELECT cust_id, cust_name

FROM customers

WHERE EXISTS (SELECT *

FROM orders

WHERE DateDiff(month, order_date, '2016-09-01') = 0

AND customers.cust_id = orders.cust_id);

Analysis

This WHERE clause uses EXISTS instead of IN. The subquery is much the same

as the one used with IN, except that this one also matches the cust_id

columns in both the customers table and the orders table so that just the

customers with orders in September 2016 are retrieved. You’ll notice that the

subquery uses SELECT *, which is usually not allowed in subqueries, although

in truth it would make no difference what columns were selected because it is

not the returned data that is being used to filter customers, but the existence of

any matching data.

So, which to use, IN or EXISTS? For the most part you can use either; both let

you use subqueries to filter data, and both can be negated using NOT to find rows

that don’t match (perhaps to find all the customers who did not order in a

specific month). The biggest practical difference between the two is

performance. Sometimes statements using EXISTS can be processed quicker

than those using IN, which is why it is often best to experiment with both

options (as well as a third option, using joins, as you will see in the next lesson).

Note: Build Queries with Subqueries Incrementally

Testing and debugging queries with subqueries can be tricky,

particularly as these statements grow in complexity. The safest way

to build (and test) queries with subqueries is to write the T-SQL

code incrementally, in much the same way as SQL Server processes

subqueries. Build and test the innermost query first. Then build and

test the outer query with hard-coded data, and only after you have

verified that it is working embed the subquery. Then test it again.

Keep repeating these steps for each additional query. This will take

just a little longer to construct your queries, but doing so saves you

lots of time later (when you try to figure out why queries are not

working) and significantly increases the likelihood of them working

the first time.

Summary

In this lesson, you learned what subqueries are and how to use them. The most

common uses for subqueries are in WHERE clauses, with IN operators, and for

populating calculated columns. You saw examples of all these types of

operations.

Lesson 14. Joining Tables

In this lesson, you learn what joins are, why they are used, and how to create

SELECT statements using them.

Understanding Joins

One of SQL’s most powerful features is the capability to join tables on the fly

within data-retrieval queries. Joins are one of the most important operations you

can perform using SQL SELECT, and a good understanding of joins and join

syntax is an extremely important part of learning SQL.

Before you can effectively use joins, you must understand relational tables and

the basics of relational database design. What follows is by no means a complete

coverage of the subject, but it should be enough to get you up and running.

Understanding Relational Tables

The best way to understand relational tables is to look at a real-world example.

Suppose you had a database table containing a product catalog, with each

catalog item in its own row. The kind of information you would store with each

item would include a product description and price, along with vendor

information about the company that creates the product.

Now suppose you had multiple catalog items created by the same vendor. Where

would you store the vendor information (things such as vendor name, address,

and contact information)? You wouldn’t want to store that data along with the

products for several reasons:

Because the vendor information is the same for each product that vendor

produces, repeating the information for each product is a waste of time and

storage space.

If vendor information changes (for example, if the vendor moves or the

area code changes), you would need to update every occurrence of the

vendor information.

When data is repeated (that is, the vendor information is used with each

product), there is a high likelihood that the data will not be entered exactly

the same way each time. Inconsistent data is extremely difficult to use in

reporting.

The key here is that having multiple occurrences of the same data is never a

good thing, and that principle is the basis for relational database design.

Relational tables are designed so information is split into multiple tables, one for

each datatype. The tables are related to each other through common values (and

thus the relational in relational design).

In our example, you can create two tables—one for vendor information and one

for product information. The vendors table contains all the vendor

information, one table row per vendor, along with a unique identifier for each

vendor. This value, called a primary key, can be a vendor ID or any other unique

value. (Primary keys were first mentioned in Lesson 1, “Understanding SQL.”)

The products table stores only product information, with no vendor-specific

information other than the vendor ID (the vendors table’s primary key). This

key, called a foreign key, relates the vendors table to the products table,

and using this vendor ID enables you to use the vendors table to find the

details about the appropriate vendor.

New Term: Foreign Key

A column in one table that contains the primary key values from

another table, thus defining the relationships between tables.

What does this do for you? Well, consider the following:

Vendor information is never repeated, so time and space are not wasted.

If vendor information changes, you can update a single record in the

vendors table. Data in related tables does not change.

Because no data is repeated, the data used is obviously consistent, making

data reporting and manipulation much simpler.

The bottom line is that relational data can be stored efficiently and manipulated

easily. Because of this, relational databases scale far better than nonrelational

databases.

New Term: Scale

Able to handle an increasing load without failing. A well-designed

database or application is said to scale well.

Why Use Joins?

As just explained, breaking data into multiple tables enables more efficient

storage, easier manipulation, and greater scalability. But these benefits come

with a price.

If data is stored in multiple tables, how can you retrieve that data with a single

SELECT statement?

The answer is to use a join. Simply put, a join is a mechanism used to associate

tables within a SELECT statement (and thus the name join). Using a special

syntax, you can join multiple tables so a single set of output is returned, and the

join associates the correct rows in each table on the fly.

Note: Maintaining Referential Integrity

It is important to understand that a join is not a physical entity—in

other words, it does not exist in the actual database tables. A join is

created by SQL Server as needed, and it persists for the duration of

the query execution.

When using relational tables, it is important that you only insert

valid data into relational columns. Going back to the example, if

products were stored in the products table with an invalid vendor

ID (one not present in the vendors table), those products would

be inaccessible because they would not be related to any vendor.

To prevent this from occurring, SQL Server can be instructed to

only allow valid values (ones present in the vendors table) in the

vendor ID column in the products table. This is known as

maintaining referential integrity, and it is achieved by specifying the

primary and foreign keys as part of the table definitions (as will be

explained in Lesson 20, “Creating and Manipulating Tables”).

Creating a Join

Creating a join is very simple. You must specify all the tables to be included and

how they are related to each other. Look at the following example:

Input

Click here to view code image

SELECT vend_name, prod_name, prod_price

FROM vendors, products

WHERE vendors.vend_id = products.vend_id

ORDER BY vend_name, prod_name;

Output

Click here to view code image

vend_name prod_name prod_price

------------------ --------------------------- ----------

ACME Bird seed 10.00

ACME Carrots 2.50

ACME Detonator 13.00

ACME Safe 50.00

ACME Sling 4.49

ACME TNT (1 stick) 2.50

ACME TNT (5 sticks) 10.00

Anvils R Us .5 ton anvil 5.99

Anvils R Us 1 ton anvil 9.99

Anvils R Us 2 ton anvil 14.99

Jet Set JetPack 1000 35.00

Jet Set JetPack 2000 55.00

LT Supplies Fuses 3.42

LT Supplies Oil can 8.99

Analysis

In the preceding code, the SELECT statement starts in the same way as all the

statements you’ve looked at thus far, by specifying the columns to be retrieved.

The big difference here is that two of the specified columns (prod_name and

prod_price) are in one table, whereas the other (vend_name) is in another

table.

Now look at the FROM clause. Unlike all the prior SELECT statements, this one

has two tables listed in the FROM clause: vendors and products. These are

the names of the two tables that are being joined in this SELECT statement. The

tables are correctly joined with a WHERE clause that instructs SQL Server to

match vend_id in the vendors table with vend_id in the products table.

You’ll notice that the columns are specified as vendors.vend_id and

products.vend_id. This fully qualified column name is required here

because if you just specify vend_id, SQL Server cannot tell which vend_id

columns you are referring to (because there are two of them, one in each table).

Caution: Fully Qualifying Column Names

You must use the fully qualified column name (table and column

separated by a period) whenever there is possible ambiguity about

to which column you are referring. SQL Server returns an error

message if you refer to an ambiguous column name without fully

qualifying it with a table name.

The Importance of the WHERE Clause

It might seem strange to use a WHERE clause to set the join relationship, but

actually there is a very good reason for this. Remember, when tables are joined

in a SELECT statement, that relationship is constructed on the fly. Nothing in the

database table definitions can instruct SQL Server how to join the tables. You

have to do that yourself. When you join two tables, what you are actually doing

is pairing every row in the first table with every row in the second table. The

WHERE clause acts as a filter to only include rows that match the specified filter

condition—the join condition, in this case. Without the WHERE clause, every row

in the first table is paired with every row in the second table, regardless of

whether they logically go together.

New Term: Cartesian Product

The results returned by a table relationship without a join condition.

The number of rows retrieved is the number of rows in the first

table multiplied by the number of rows in the second table.

To understand this, look at the following SELECT statement and output:

Input

Click here to view code image

SELECT vend_name, prod_name, prod_price

FROM vendors, products

ORDER BY vend_name, prod_name;

Output

Click here to view code image

vend_name prod_name prod_price

------------------ --------------------------- ----------

ACME .5 ton anvil 5.99

ACME 1 ton anvil 9.99

ACME 2 ton anvil 14.99

ACME Bird seed 10.00

ACME Carrots 2.50

ACME Detonator 13.00

ACME Fuses 3.42

ACME JetPack 1000 35.00

ACME JetPack 2000 55.00

ACME Oil can 8.99

ACME Safe 50.00

ACME Sling 4.49

ACME TNT (1 stick) 2.50

ACME TNT (5 sticks) 10.00

Anvils R Us .5 ton anvil 5.99

Anvils R Us 1 ton anvil 9.99

Anvils R Us 2 ton anvil 14.99

Anvils R Us Bird seed 10.00

Anvils R Us Carrots 2.50

Anvils R Us Detonator 13.00

Anvils R Us Fuses 3.42

Anvils R Us JetPack 1000 35.00

Anvils R Us JetPack 2000 55.00

Anvils R Us Oil can 8.99

Anvils R Us Safe 50.00

Anvils R Us Sling 4.49

Anvils R Us TNT (1 stick) 2.50

Anvils R Us TNT (5 sticks) 10.00

Furball Inc. .5 ton anvil 5.99

Furball Inc. 1 ton anvil 9.99

Furball Inc. 2 ton anvil 14.99

Furball Inc. Bird seed 10.00

Furball Inc. Carrots 2.50

Furball Inc. Detonator 13.00

Furball Inc. Fuses 3.42

Furball Inc. JetPack 1000 35.00

Furball Inc. JetPack 2000 55.00

Furball Inc. Oil can 8.99

Furball Inc. Safe 50.00

Furball Inc. Sling 4.49

Furball Inc. TNT (1 stick) 2.50

Furball Inc. TNT (5 sticks) 10.00

Jet Set .5 ton anvil 5.99

Jet Set 1 ton anvil 9.99

Jet Set 2 ton anvil 14.99

Jet Set Bird seed 10.00

Jet Set Carrots 2.50

Jet Set Detonator 13.00

Jet Set Fuses 3.42

Jet Set JetPack 1000 35.00

Jet Set JetPack 2000 55.00

Jet Set Oil can 8.99

Jet Set Safe 50.00

Jet Set Sling 4.49

Jet Set TNT (1 stick) 2.50

Jet Set TNT (5 sticks) 10.00

Jouets Et Ours .5 ton anvil 5.99

Jouets Et Ours 1 ton anvil 9.99

Jouets Et Ours 2 ton anvil 14.99

Jouets Et Ours Bird seed 10.00

Jouets Et Ours Carrots 2.50

Jouets Et Ours Detonator 13.00

Jouets Et Ours Fuses 3.42

Jouets Et Ours JetPack 1000 35.00

Jouets Et Ours JetPack 2000 55.00

Jouets Et Ours Oil can 8.99

Jouets Et Ours Safe 50.00

Jouets Et Ours Sling 4.49

Jouets Et Ours TNT (1 stick) 2.50

Jouets Et Ours TNT (5 sticks) 10.00

LT Supplies .5 ton anvil 5.99

LT Supplies 1 ton anvil 9.99

LT Supplies 2 ton anvil 14.99

LT Supplies Bird seed 10.00

LT Supplies Carrots 2.50

LT Supplies Detonator 13.00

LT Supplies Fuses 3.42

LT Supplies JetPack 1000 35.00

LT Supplies JetPack 2000 55.00

LT Supplies Oil can 8.99

LT Supplies Safe 50.00

LT Supplies Sling 4.49

LT Supplies TNT (1 stick) 2.50

LT Supplies TNT (5 sticks) 10.00

Analysis

As you can see in the preceding output, the Cartesian product is seldom what

you want. The data returned here has matched every product with every vendor,

including products with the incorrect vendor (and even vendors with no products

at all).

Caution: Don’t Forget the WHERE Clause

Make sure all your joins have WHERE clauses; otherwise, SQL

Server returns far more data than you want. Similarly, make sure

your WHERE clauses are correct. An incorrect filter condition causes

SQL Server to return incorrect data.

Tip: Cross Joins

Sometimes you’ll hear the type of join that returns a Cartesian

product referred to as a cross join.

In Lesson 13, “Working with Subqueries,” you saw two ways to obtain a list of

customers who ordered products in September of 2016, and both solutions used

subqueries (one using IN and one using EXISTS). Here is a third solution, this

time using an inner join:

Input

Click here to view code image

SELECT customers.cust_id, customers.cust_name

FROM customers, orders

WHERE DateDiff(month, order_date, '2016-09-01') = 0

AND customers.cust_id = orders.cust_id;

Output

Click here to view code image

cust_id cust_name

----------- ------------------

10001 Coyote Inc.

10003 Wascals

10004 Yosemite Place

Inner Joins

The join you have been using so far is called an equijoin—a join based on the

testing of equality between two tables. This kind of join is also called an inner

join. In fact, you may use a slightly different syntax for these joins, specifying

the type of join explicitly. The following SELECT statement returns the exact

same data as the preceding example:

Input

Click here to view code image

SELECT customers.cust_id, customers.cust_name

FROM customers INNER JOIN orders ON

customers.cust_id = orders.cust_id

WHERE DateDiff(month, order_date, '2016-09-01') = 0

AND customers.cust_id = orders.cust_id;

Analysis

The SELECT in this statement is the same as the preceding SELECT statement,

but the FROM clause is different. Here, the relationship between the two tables is

part of the FROM clause specified as INNER JOIN. When using this syntax, the

join condition is specified using the special ON clause instead of a WHERE

clause. The actual condition passed to ON is the same as would be passed to

WHERE.

Note: Output Ordering

The WHERE syntax join and the INNER JOIN syntax join return

the exact same results. However, you may notice that the two forms

of joins return results in different orders. Of course, if you specify

an ORDER BY clause then regardless of the syntax used the data

will be sorted as specified.

Tip: Which Syntax to Use?

Per the ANSI SQL specification, use of the INNER JOIN syntax is

preferable. Although using the WHERE clause to define joins is

indeed simpler, using explicit join syntax ensures that you will

never forget the join condition, and in some cases it can impact

performance, too.

Joining Multiple Tables

SQL imposes no limit to the number of tables that may be joined in a SELECT

statement. The basic rules for creating the join remain the same. First, list all the

tables and then define the relationship between each. Here is an example:

Input

Click here to view code image

SELECT prod_name, vend_name, prod_price, quantity

FROM orderitems, products, vendors

WHERE products.vend_id = vendors.vend_id

AND orderitems.prod_id = products.prod_id

AND order_num = 20005;

Output

Click here to view code image

prod_name vend_name prod_price quantity

-------------------- ----------------- ------------ -----------

.5 ton anvil Anvils R Us 5.99 10

1 ton anvil Anvils R Us 9.99 3

TNT (5 sticks) ACME 10.00 5

Bird seed ACME 10.00 1

Analysis

This example displays the items in order number 20005. Order items are stored

in the orderitems table. Each product is stored by its product ID, which

refers to a product in the products table. The products are linked to the

appropriate vendor in the vendors table by the vendor ID, which is stored with

each product record. The FROM clause here lists the three tables, and the WHERE

clause defines both of those join conditions. An additional WHERE condition is

then used to filter just the items for order 20005.

Caution: Performance Considerations

SQL Server processes joins at runtime, relating each table as

specified. This process can become very resource intensive, so be

careful not to join tables unnecessarily. The more tables you join,

the more performance degrades.

This degradation can be dramatically decreased (perhaps even

eliminated) by effectively creating indexes for all foreign key

columns.

Now would be a good time to revisit the example from Lesson 13. As you will

recall, this SELECT statement returns a list of customers who ordered product

TNT2:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers

WHERE cust_id IN (SELECT cust_id

FROM orders

WHERE order_num IN (SELECT order_num

FROM orderitems

WHERE prod_id = 'TNT2'));

As mentioned in Lesson 13, using subqueries might not always be the most

efficient way to perform complex SELECT operations. So, as promised, here is

the same query using joins:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers, orders, orderitems

WHERE customers.cust_id = orders.cust_id

AND orderitems.order_num = orders.order_num

AND prod_id = 'TNT2';

Output

Click here to view code image

cust_nam cust_contact

--------------------- ---------------

Coyote Inc. Y Lee

Yosemite Place Y Sam

Analysis

As explained in Lesson 13, returning the data needed in this query requires the

use of three tables. But instead of using them within nested subqueries, here two

joins are used to connect the tables. There are three WHERE clause conditions

here. The first two connect the tables in the join, and the last one filters the data

for product TNT2.

Tip: It Pays to Experiment

As you can see, there is often more than one way to perform any

given SQL operation. And there is rarely a definitive right or wrong

way. Performance can be affected by the type of operation, the

amount of data in the tables, whether indexes and keys are present,

and a whole slew of other criteria. Therefore, it is often worth

experimenting with different selection mechanisms to find the one

that works best for you.

Summary

Joins are one of the most important and powerful features in SQL, and using

them effectively requires a basic understanding of relational database design. In

this lesson, you learned some of the basics of relational database design as an

introduction to learning about joins. You also learned how to create an equijoin

(also known as an inner join), which is the most commonly used form of join. In

the next lesson, you learn how to create other types of joins.

Lesson 15. Creating Advanced Joins

In this lesson, you learn all about additional join types—what they are and how

to use them. You also learn how to use table aliases and how to use aggregate

functions with joined tables.

Using Table Aliases

Back in Lesson 9, “Creating Calculated Fields,” you learned how to use aliases

to refer to retrieved table columns. The syntax to alias a column looks like this:

Input

Click here to view code image

SELECT RTrim(vend_name) + ' (' + RTrim(vend_country) + ')'

AS vend_title

FROM vendors

ORDER BY vend_name;

In addition to using aliases for column names and calculated fields, SQL also

enables you to alias table names. There are two primary reasons to do this:

To shorten the SQL syntax

To enable multiple uses of the same table within a single SELECT

statement

Take a look at the following SELECT statement. It is basically the same

statement as an example used in the previous lesson, but it has been modified to

use aliases:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers AS c, orders AS o, orderitems AS oi

WHERE c.cust_id = o.cust_id

AND oi.order_num = o.order_num

AND prod_id = 'TNT2';

Analysis

You’ll notice that the three tables in the FROM clauses all have aliases.

customers AS c establishes c as an alias for customers, and so on. This

enables you to use the abbreviated c instead of the full text customers. In this

example, the table aliases were used only in the WHERE clause, but aliases are

not limited to just WHERE. You can use aliases in the SELECT list, the ORDER

BY clause, and in any other part of the statement as well.

Note: Execution Time Only

It is worth noting that table aliases are only used during query

execution. Unlike column aliases, table aliases are never returned

back to the client in the results.

Using Different Join Types

So far, you have used only simple joins known as inner joins or equijoins. You’ll

now take a look at three additional join types: the self join, the natural join, and

the outer join.

Self Joins

As mentioned earlier, one of the primary reasons to use table aliases is to be able

to refer to the same table more than once in a single SELECT statement. An

example will demonstrate this.

Suppose that a problem is found with a product (item ID DTNTR), and you want

to know all the products made by the same vendor so as to determine whether

the problem applies to them too. This query requires that you first find out which

vendor creates item DTNTR and then find which other products are made by that

vendor. The following is one way to approach this problem:

Input

Click here to view code image

SELECT prod_id, prod_name

FROM products

WHERE vend_id = (SELECT vend_id

FROM products

WHERE prod_id = 'DTNTR');

Output

Click here to view code image

prod_id prod_name

---------- --------------------

DTNTR Detonator

FB Bird seed

FC Carrots

SAFE Safe

SLING Sling

TNT1 TNT (1 stick)

TNT2 TNT (5 sticks)

Analysis

This first solution uses subqueries. The inner SELECT statement does a simple

retrieval to return the vend_id of the vendor that makes item DTNTR. That ID

is the one used in the WHERE clause of the outer query, so all items produced by

that vendor are retrieved. (You learned all about subqueries in Lesson 13. Refer

to that lesson for more information.)

Now look at the same query using a join:

Input

Click here to view code image

SELECT p1.prod_id, p1.prod_name

FROM products AS p1, products AS p2

WHERE p1.vend_id = p2.vend_id

AND p2.prod_id = 'DTNTR';

Output

Click here to view code image

prod_id prod_name

---------- --------------------

DTNTR Detonator

FB Bird seed

FC Carrots

SAFE Safe

SLING Sling

TNT1 TNT (1 stick)

TNT2 TNT (5 sticks)

Analysis

The two tables needed in this query are actually the same table, so the

products table appears in the FROM clause twice. Although this is perfectly

legal, any references to table products would be ambiguous because SQL

Server could not know to which instance of the products table you are

referring.

To resolve this problem, table aliases are used. The first occurrence of

products has an alias of p1, and the second has an alias of p2. Now those

aliases can be used as table names. The SELECT statement, for example, uses

the p1 prefix to explicitly state the full name of the desired columns. If it did

not, SQL Server would return an error because there are two columns named

prod_id and prod_name. It cannot know which one you want (even though,

in truth, they are one and the same). The WHERE clause first joins the tables (by

matching vend_id in p1 to vend_id in p2) and then filters the data by

prod_id in the second table to return only the desired data.

Tip: Self Joins Instead of Subqueries

Self joins are often used to replace statements using subqueries that

retrieve data from the same table as the outer statement. Although

the end result is the same, sometimes these joins execute far more

quickly than do subqueries. It is usually worth experimenting with

both to determine which performs better.

Natural Joins

Whenever tables are joined, at least one column appears in more than one table

(the columns being joined). Standard joins (the inner joins you learned about in

the previous lesson) return all data, even multiple occurrences of the same

column. A natural join simply eliminates those multiple occurrences so only one

of each column is returned.

How does it do this? The answer is, it doesn’t—you do it. A natural join is a join

in which you select only columns that are unique. This is typically done using a

wildcard (SELECT *) for one table and explicit subsets of the columns for all

other tables. The following is an example:

Input

Click here to view code image

SELECT c.*, o.order_num, o.order_date,

oi.prod_id, oi.quantity, OI.item_price

FROM customers AS c, orders AS o, orderitems AS oi

WHERE c.cust_id = o.cust_id

AND oi.order_num = o.order_num

AND prod_id = 'FB';

Analysis

In this example, a wildcard is used for the first table only. All other columns are

explicitly listed so no duplicate columns are retrieved.

The truth is, every inner join you have created thus far is actually a natural join,

and you will probably never even need an inner join that is not a natural join.

Outer Joins

Most joins relate rows in one table with rows in another. But occasionally, you

want to include rows that have no related rows. For example, you might use

joins to accomplish the following tasks:

Count how many orders each customer placed, including customers who

have yet to place an order.

List all products with order quantities, including products not ordered by

anyone.

Calculate average sale sizes, taking into account customers who have not

yet placed an order.

In each of these examples, the join includes table rows that have no associated

rows in the related table. This type of join is called an outer join.

The following SELECT statement is a simple inner join. It retrieves a list of all

customers and their orders:

Input

Click here to view code image

SELECT customers.cust_id, orders.order_num

FROM customers INNER JOIN orders

ON customers.cust_id = orders.cust_id;

Outer join syntax is similar. To retrieve a list of all customers, including those

who have placed no orders, you can do the following:

Input

Click here to view code image

SELECT customers.cust_id, orders.order_num

FROM customers LEFT OUTER JOIN orders

ON customers.cust_id = orders.cust_id;

Output

cust_id order_num

----------- -----------

10001 20005

10001 20009

10002 NULL

10003 20006

10004 20007

10005 20008

Analysis

Like the inner join shown in the previous lesson, this SELECT statement uses

the keywords OUTER JOIN to specify the join type (instead of specifying it in

the WHERE clause). But unlike inner joins, which relate rows in both tables,

outer joins also include rows with no related rows. When using OUTER JOIN

syntax, you must use the RIGHT or LEFT keyword to specify the table from

which to include all rows (RIGHT for the one on the right of OUTER JOIN, and

LEFT for the one on the left). The previous example uses LEFT OUTER JOIN

to select all the rows from the table on the left in the FROM clause (the

customers table). To select all the rows from the table on the right, you use a

RIGHT OUTER JOIN, as shown in this example:

Input

Click here to view code image

SELECT customers.cust_id, orders.order_num

FROM customers RIGHT OUTER JOIN orders

ON orders.cust_id = customers.cust_id;

Tip: Outer Join Types

There are two basic forms of outer joins—the left outer join and the

right outer join. The only difference between them is the order of

the tables they are relating. In other words, a left outer join can be

turned into a right outer join simply by reversing the order of the

tables in the FROM or WHERE clause. As such, the two types of

outer joins can be used interchangeably, and the decision about

which one is used is based purely on convenience.

Note: Non-ANSI Outer Joins

In the previous lesson, you learned two ways to write an inner join:

using a simplified WHERE clause and using INNER JOIN syntax.

In this lesson, you’ve seen the ANSI-style OUTER JOIN syntax,

but not a simplified WHERE clause outer join.

The truth is there is a simplified syntax for outer joins using a

WHERE clause. I’ll show it to you, but must first tell you to never

use it.

Here’s the simplified outer join:

Click here to view code image

SELECT customers.cust_id, orders.order_num

FROM customers, orders

WHERE customers.cust_id *= orders.cust_id;

The *= instructs SQL Server to retrieve all the rows from the first

table (customers, the table nearer the *) and only related rows

from the second table (orders, the table nearer the =). Therefore,

*= creates a left outer join. Similarly, =* would create a right outer

join (because * is on the right).

As stated previously, it is a simpler syntax. So why not use it? This

form of syntax is not part of the ANSI standard and as of SQL

Server 2005 is no longer supported by default in SQL Server. The

only reason I mention it is so that you’ll know what it is if you come

across it.

This does not apply to the simplified inner join syntax (shown in the

last lesson), which Microsoft has not announced any intention of

dropping support for.

There is one other form of outer join worth noting, although you will likely

rarely find a use for it. The FULL OUTER JOIN is used to retrieve the related

rows from both tables, as well as the unrelated rows from each (these will have

NULL values for the unrelated columns in the other table). The syntax for a

FULL OUTER JOIN is the same as the previously shown outer joins,

obviously substituting RIGHT and LEFT for FULL.

Using Joins with Aggregate Functions

As you learned in Lesson 11, “Summarizing Data,” aggregate functions are used

to summarize data. Although all the examples of aggregate functions thus far

only summarized data from a single table, these functions can also be used with

joins.

To demonstrate this, let’s look at an example. You want to retrieve a list of all

customers and the number of orders that each has placed. The following code

uses the Count() function to achieve this:

Input

Click here to view code image

SELECT customers.cust_name,

customers.cust_id,

Count(orders.order_num) AS num_ord

FROM customers INNER JOIN orders

ON customers.cust_id = orders.cust_id

GROUP BY customers.cust_name,

customers.cust_id;

Output

Click here to view code image

cust_name cust_id num_ord

----------------------- ----------- -----------

Coyote Inc. 10001 2

Wascals 10003 1

Yosemite Place 10004 1

E Fudd 10005 1

Analysis

This SELECT statement uses INNER JOIN to relate the customers and

orders tables to each other. The GROUP BY clause groups the data by

customer, and so the function call Count(orders.order_num) counts the

number of orders for each customer and returns it as num_ord.

Aggregate functions can be used just as easily with other join types. See the

following example:

Input

Click here to view code image

SELECT customers.cust_name,

customers.cust_id,

Count(orders.order_num) AS num_ord

FROM customers LEFT OUTER JOIN orders

ON customers.cust_id = orders.cust_id

GROUP BY customers.cust_name,

customers.cust_id;

Output

Click here to view code image

cust_name cust_id num_ord

----------------------- ----------- -----------

Coyote Inc. 10001 2

Mouse House 10002 0

Wascals 10003 1

Yosemite Place 10004 1

E Fudd 10005 1

Analysis

This example uses a left outer join to include all customers, even those who have

not placed any orders. The results show that customer Mouse House (with no

orders) is also included this time.

Note: Null Value Elimination Warning

Depending on the database client you are using, you may have seen

the following warning generated by the previous T-SQL statement:

Click here to view code image

Warning: Null value is eliminated by an aggregate or other

SET operation.

This is not an error message; it is just an informational warning, and

it is telling you that a row (in this case, Mouse House) should

have returned NULL because there are no orders. However, because

an aggregate function was used (the Count() function), that NULL

was converted to a number (in this case, 0).

Using Joins and Join Conditions

Before wrapping up this two-lesson discussion on joins, it is worthwhile to

summarize some key points regarding joins and their use:

Pay careful attention to the type of join being used. More often than not,

you’ll want an inner join, but there are often valid uses for outer joins too.

Make sure you use the correct join condition, or you’ll return incorrect

data.

Make sure you always provide a join condition, or you’ll end up with the

Cartesian product.

You may include multiple tables in a join and even have different join

types for each. Although this is legal and often useful, make sure you test

each join separately before testing them together. This makes

troubleshooting far simpler.

Summary

This lesson was a continuation of the previous lesson on joins. This lesson

started by teaching you how and why to use aliases and then continued with a

discussion on different join types and the various forms of syntax used with

each. You also learned how to use aggregate functions with joins, and some

important do’s and don’ts to keep in mind when working with joins.

Lesson 16. Combining Queries

In this lesson, you learn how to use the UNION operator to combine multiple

SELECT statements into one result set.

Understanding Combined Queries

Most SQL queries contain a single SELECT statement that returns data from one

or more tables. T-SQL also enables you to perform multiple queries (multiple

SELECT statements) and return the results as a single query result set. These

combined queries are usually known as unions or compound queries.

There are basically two scenarios in which you’d use combined queries:

To return similarly structured data from different tables in a single query

To perform multiple queries against a single table, returning the data as

one query

Tip: Combining Queries and Multiple WHERE Conditions

For the most part, combining two queries to the same table

accomplishes the same thing as a single query with multiple WHERE

clause conditions. In other words, any SELECT statement with

multiple WHERE clauses can also be specified as a combined query,

as you’ll see in the section that follows. However, the performance

of each of the two techniques can vary based on the queries used.

As such, it is always good to experiment to determine which is

preferable for specific queries.

Creating Combined Queries

SQL queries are combined using the UNION operator. Using UNION, you can

specify multiple SELECT statements and combine their results into a single

result set.

Using UNION

Using UNION is simple enough. All you do is specify each SELECT statement

and place the keyword UNION between each.

Let’s look at an example. You need a list of all products costing 5 or less. You

also want to include all products made by vendors 1001 and 1002, regardless

of price. Of course, you can create a WHERE clause that will do this, but this time

you’ll use a UNION instead.

As just explained, creating a UNION involves writing multiple SELECT

statements. First look at the individual statements:

Input

Click here to view code image

SELECT vend_id, prod_id, prod_price

FROM products

WHERE prod_price <= 5;

Output

Click here to view code image

vend_id prod_id prod_price

----------- ---------- ----------

1003 FC 2.50

1002 FU1 3.42

1003 SLING 4.49

1003 TNT1 2.50

Input

Click here to view code image

SELECT vend_id, prod_id, prod_price

FROM products

WHERE vend_id IN (1001,1002);

Output

Click here to view code image

vend_id prod_id prod_price

----------- ---------- ----------

1001 ANV01 5.99

1001 ANV02 9.99

1001 ANV03 14.99

1002 FU1 3.42

1002 OL1 8.99

Analysis

The first SELECT retrieves all products with a price of no more than 5. The

second SELECT uses IN to find all products made by vendors 1001 and 1002.

To combine these two statements, do the following:

Input

Click here to view code image

SELECT vend_id, prod_id, prod_price

FROM products

WHERE prod_price <= 5

UNION

SELECT vend_id, prod_id, prod_price

FROM products

WHERE vend_id IN (1001,1002);

Output

Click here to view code image

vend_id prod_id prod_price

----------- ---------- ----------

1001 ANV01 5.99

1001 ANV02 9.99

1001 ANV03 14.99

1002 FU1 3.42

1002 OL1 8.99

1003 FC 2.50

1003 SLING 4.49

1003 TNT1 2.50

Analysis

The preceding statements are made up of both of the previous SELECT

statements separated by the UNION keyword. UNION instructs SQL Server to

execute both SELECT statements and combine the output into a single query

result set.

As a point of reference, here is the same query using multiple WHERE clauses

instead of a UNION:

Input

Click here to view code image

SELECT vend_id, prod_id, prod_price

FROM products

WHERE prod_price <= 5

OR vend_id IN (1001,1002);

In this simple example, the UNION might actually be more complicated than

using a WHERE clause. But with more complex filtering conditions, or if the data

were being retrieved from multiple tables (not just a single table), the UNION

could have made the process much simpler.

UNION Rules

As you can see, unions are very easy to use. But a few rules govern exactly

which can be combined:

A UNION must be composed of two or more SELECT statements, each

separated by the keyword UNION (therefore, if you were combining four

SELECT statements, you would use three UNION keywords).

Each query in a UNION must contain the same columns, expressions, or

aggregate functions, and they must be listed in the same order. (Other

DBMSs do not impose this restriction and allow columns to be in any

order as long as they are all present.)

Column datatypes must be compatible: They need not be the exact same

type, but they must be of a type that SQL Server can implicitly convert (for

example, different numeric types or different date types).

Aside from these basic rules and restrictions, unions can be used for any data

retrieval tasks.

Including or Eliminating Duplicate Rows

Go back to the preceding section titled “Using UNION” and look at the sample

SELECT statements used. You’ll notice that when they’re executed individually,

the first SELECT statement returns four rows, and the second SELECT

statement returns five rows. However, when the two SELECT statements are

combined with a UNION, only eight rows are returned, not nine.

The UNION automatically removes any duplicate rows from the query result set

(in other words, it behaves just as multiple WHERE clause conditions in a single

SELECT would). Because vendor 1002 creates a product that costs less than 5,

that row was returned by both SELECT statements. When the UNION was used,

the duplicate row was eliminated.

This is the default behavior of UNION, but you can change this if you so desire.

If you do, in fact, want all occurrences of all matches returned, you can use

UNION ALL instead of UNION.

Look at the following example:

Input

Click here to view code image

SELECT vend_id, prod_id, prod_price

FROM products

WHERE prod_price <= 5

UNION ALL

SELECT vend_id, prod_id, prod_price

FROM products

WHERE vend_id IN (1001,1002);

Output

Click here to view code image

vend_id prod_id prod_price

----------- ---------- ----------

1003 FC 2.50

1002 FU1 3.42

1003 SLING 4.49

1003 TNT1 2.50

1001 ANV01 5.99

1001 ANV02 9.99

1001 ANV03 14.99

1002 FU1 3.42

1002 OL1 8.99

Analysis

Using UNION ALL, SQL Server does not eliminate duplicates. Therefore, the

preceding example returns nine rows, one of them occurring twice.

Tip: UNION versus WHERE

The beginning of this lesson said that UNION almost always

accomplishes the same thing as multiple WHERE conditions. UNION

ALL is the form of UNION that accomplishes what cannot be done

with WHERE clauses. If you do, in fact, want all occurrences of

matches for every condition (including duplicates), you must use

UNION ALL and not WHERE.

Sorting Combined Query Results

SELECT statement output is sorted using the ORDER BY clause. When

combining queries with a UNION, you may use only one ORDER BY clause, and

it must occur after the final SELECT statement. There is very little point in

sorting part of a result set one way and part another way; therefore, multiple

ORDER BY clauses are not allowed.

The following example sorts the results returned by the previously used UNION:

Input

Click here to view code image

SELECT vend_id, prod_id, prod_price

FROM products

WHERE prod_price <= 5

UNION

SELECT vend_id, prod_id, prod_price

FROM products

WHERE vend_id IN (1001,1002)

ORDER BY vend_id, prod_price;

Output

Click here to view code image

vend_id prod_id prod_price

----------- ---------- ----------

1001 ANV01 5.99

1001 ANV02 9.99

1001 ANV03 14.99

1002 FU1 3.42

1002 OL1 8.99

1003 FC 2.50

1003 TNT1 2.50

1003 SLING 4.49

Analysis

This UNION takes a single ORDER BY clause after the final SELECT statement.

Even though the ORDER BY appears to only be a part of that last SELECT

statement, SQL Server will in fact use it to sort all the results returned by all the

SELECT statements.

Note: Combining Different Tables

For the sake of simplicity, all the examples in this lesson combined

queries using the same table. However, everything you learned here

also applies to using UNION to combine queries of different tables.

Summary

In this lesson, you learned how to combine SELECT statements with the UNION

operator. Using UNION, you can return the results of multiple queries as one

combined query, either including or excluding duplicates. The use of UNION can

greatly simplify complex WHERE clauses and retrieving data from multiple

tables.

Lesson 17. Full-Text Searching

In this lesson, you learn how to use SQL Server’s full-text searching capabilities

to perform sophisticated data querying and selection.

Understanding Full-Text Searching

In Lesson 8, “Using Wildcard Filtering,” you were introduced to the LIKE

keyword, which is used to match text (and partial text) via wildcard operators.

Using LIKE it is possible to locate rows that contain specific values or parts of

values, regardless of the location of those values within row columns.

But as useful as these search mechanisms are, they have several very important

limitations:

Performance—Wildcard matching usually requires that SQL Server try

matching each and every row in a table (and table indexes are rarely of use

in these searches). As such, these searches can be very time-consuming as

the number of rows to be searched grows.

Explicit control—Using wildcard matching, it is very difficult (and not

always possible) to explicitly control what is and what is not matched. An

example of this is a search specifying a word that must be matched, a word

that must not be matched, and a word that may or may not be matched

depending on whether the first word is indeed matched.

Intelligent results—Although wildcard-based searching provides for very

flexible searching, it does not provide an intelligent way to select results.

For example, searching for a specific word would return all rows that

contain that word, but not distinguish between rows that contain a single

match and those that contain multiple matches (ranking them as potentially

better matches). Similarly, searches for a specific word would not find

rows that do not contain that word but do contain other related words.

All of these limitations and more are addressed by full-text searching. When

full-text searching is used, SQL Server does not need to look at each row

individually, analyzing and processing each word individually. Rather, an index

of the words (in specified columns) is created by SQL Server, and searches can

be made against those words. SQL Server can thus quickly and efficiently

determine which words match (which rows contain them), which don’t, how

often they match, and so on.

Setting Up Full-Text Searching

Here’s a list of requirements to perform full-text searches:

Support for full-text searching must be enabled for the relevant database.

A catalog must be defined (this is where full-text data is stored).

A full-text index must be created for the tables and columns to be indexed.

After indexing, you can use SELECT with the FREETEXT and CONTAINS

predicates to actually perform the searches.

Enabling Full-Text Searching Support

After a database has been created, support for full text must be enabled before

any full-text operations can be performed. To enable full-text support, use the

sp_fulltext_database stored procedure. This stored procedure updates

the currently selected database, so be sure to USE the correct database before

issuing this statement:

Input

Click here to view code image

EXEC sp_fulltext_database 'enable';

Analysis

sp_fulltext_database accepts a parameter specifying whether to

enable or disable full-text support.

Note: Enabling Full-Text Search at Database Creation Time

When creating a new database in Microsoft SQL Server

Management Studio, you can check the Use Full-Text Indexing box,

which causes the previously mentioned stored procedure to be

automatically executed.

Tip: Not Sure If Full Text Is Enabled?

If you don’t know whether full-text support is enabled, just run the

stored procedure anyway. If full-text support is not enabled, the

stored procedure will enable it. If full-text support is already

enabled, the stored procedure will do nothing at all.

Creating a Full-Text Catalog

As already explained, SQL Server stores full-text data in a catalog (a file that

needs to be created). A single catalog can be used for multiple tables and

indexes, so feel free to use an existing catalog if one exists. Alternatively, just

create a catalog using CREATE FULLTEXT CATALOG:

Input

Click here to view code image

CREATE FULLTEXT CATALOG catalog_learnsql;

Analysis

Here, a catalog named catalog_learnsql is created in the default catalog

location. To specify the actual file location, the IN PATH attribute could have

been specified.

Creating a Full-Text Index

Now that a catalog has been created, you can define the actual full-text indexes.

Indexes are created using CREATE FULLTEXT INDEX, as seen here:

Input

Click here to view code image

CREATE FULLTEXT INDEX ON productnotes(note_text)

KEY INDEX pk_productnotes

ON catalog_learnsql;

Analysis

This creates a full-text index on table productnotes, indexing the

note_text column. The key with which to uniquely identify rows is required,

so KEY INDEX is used to provide the name of the table’s primary key,

pk_productnotes. Finally, the ON clause specifies the catalog to be used to

store full-text data, and here the just created catalog is used.

Note: Possible Warning Messages

If the above CREATE FULLTEXT statement generates column

warnings, don’t worry, it’ll still work.

More than one column may be indexed if needed. To do this, simply specify the

column names (comma delimited).

Note: Defining a Default Catalog

When CREATE FULLTEXT CATALOG is used to create a new

catalog, the optional AS DEFAULT clause may be specified. Doing

so makes the newly created catalog the default to be used for

subsequently created full-text indexes, which would therefore mean

that the final ON clause in CREATE FULLTEXT INDEX could be

omitted.

Now that the full-text index has been created, any existing table data is indexed,

and any data INSERT, UPDATE, and DELETE operations performed against

table productnotes will force that index to be updated.

Tip: Don’t Use Full-Text Indexes When Importing Data

Updating indexes takes time—not a lot of time, but time

nonetheless. And updating full-text indexes takes even longer. If

you are importing data into a new table, you should not enable

FULLTEXT indexing at that time. Rather, first import all of the data,

and then modify the table to define FULLTEXT. This makes for a

much faster data import (and the total time needed to index all data

will be less than the sum of the time needed to index each row

individually).

Managing Catalogs and Indexes

Catalogs and indexes may be updated using ALTER FULLTEXT and dropped

using DROP FULLTEXT. In practice, these statements are rarely used, with one

exception. If the catalog or index becomes corrupt (returning inconsistent

results) or is too slow, it may benefit from being rebuilt. You can do this as

follows:

Input

Click here to view code image

ALTER FULLTEXT CATALOG catalog_learnsql REBUILD;

Analysis

This statement deletes and rebuilds the catalog indexes, effectively forcing a

complete reindexing.

If you would like to learn more about existing catalogs and indexes, a series of

system views can be used:

Input

Click here to view code image

SELECT * FROM sys.fulltext_catalogs;

Analysis

This statement returns information about the currently used catalog, including

physical file location and whether or not it is marked as the default catalog.

Input

Click here to view code image

SELECT * FROM sys.fulltext_indexes;

Analysis

This statement returns information about defined indexes, including the ID of the

catalog used, whether or not the indexes are updated automatically, and when the

last update started and ended.

Tip: The FulltextCatalogProperty() Function

A system function named FulltextCatalogProperty() can

be used to obtain information about catalogs.

FulltextCatalogProperty() accepts a catalog name and

the property to be checked. The two most important properties are

IndexSize and PopulateStatus (which lets you know if the

index is up to date, currently being built, and more).

Performing Full-Text Searches

When data has been indexed, full-text searches may be performed using two

predicates:

FREETEXT performs simple searches, matching by meaning as opposed to

an exact text match.

CONTAINS performs searches for words or phrases, taking into account

proximity, derived words, and synonyms.

Both FREETEXT and CONTAINS are used in SELECT statement WHERE

clauses.

Searching Using FREETEXT

FREETEXT is used to search for rows that contain words or phrases that might

mean the same as (or are similar to) a specified phrase.

Let’s look at an example. The following is a simple LIKE wildcard SELECT:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE note_text LIKE '%rabbit food%';

Analysis

This statement looks for the phrase rabbit food within column

note_text. No rows are returned because that phrase does not appear in any

rows.

Now the same search using FREETEXT full-text searching:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE FREETEXT(note_text, 'rabbit food');

Output

Click here to view code image

note_id note_text

---------- -------------------------------------------------

104 Quantity varies, sold by the sack load. All

guaranteed to be bright and orange, and suitable

for use as rabbit bait.

110 Customer complaint: rabbit has been able to detect

trap, food apparently less effective now.

Analysis

FREETEXT(note_text, 'rabbit food') means “perform a

FREETEXT lookup on column note_text looking for anything that could

mean rabbit food.” Two rows were retrieved—one containing both the

words rabbit and food, but not as a joined phrase, and the other containing

rabbit and a context that infers food, even though the word food is not

actually present.

As you can see, FREETEXT full-text searching is very easy to use.

Unfortunately, that simplicity comes at a cost, and FREETEXT searches lack the

more sophisticated control you may need from full-text searching. This is why

there is another predicate, named CONTAINS.

Note: Support for Other Languages

By default, FREETEXT uses the default catalog language to

determine which words to index and which to ignore (words such as

it and the are typically ignored because their frequency would

distort results). To specify an alternate language, simply pass that

language name or ID as a third parameter to FULLTEXT(). The

specified language must be one of those listed in the

sys.syslanguages system table. You can list the languages

using the following statement:

Click here to view code image

SELECT * FROM sys.syslanguages;

Searching Using CONTAINS

CONTAINS is used to search for rows that contain words, phrases, partial

phrases, words with the same stem, proximity searches, synonyms (using a

thesaurus lookup), and more.

Let’s start with a simple example:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE CONTAINS(note_text, 'handsaw');

Output

Click here to view code image

note_id note_text

----------- -----------------------------------------------

112 Customer complaint: Circular hole in safe floor

can apparently be easily cut with handsaw.

Analysis

WHERE CONTAINS(note_text, 'handsaw') means “find the word

handsaw in column note_text.”

Tip: CONTAINS or Like?

WHERE CONTAINS(note_text, 'handsaw') is

functionality identical to LIKE note_text = '%handsaw%'.

However, the CONTAINS search will typically be far quicker,

especially as the size of your tables increases.

CONTAINS also supports the use of wildcards, as seen here:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE CONTAINS(note_text, '"anvil*"');

Output

Click here to view code image

note_id note_text

----------- -------------------------------------------------

108 Multiple customer returns, anvils failing to drop

fast enough or falling backwards on purchaser.

Recommend that customer considers using heavier

anvils.

Analysis

'"anvil*"' means “match any word that starts with anvil.” Note that

unlike LIKE, full-text searching uses * as the wildcard character (instead of %).

Wildcards may be used at the beginning or end of a string.

Caution: Watch Those Quotes

The search term in the last example was '"anvil*"', with the

usual single quotes around "anvil*" (double quotes). When you

pass simple text to CONTAINS, that text is then enclosed within

single quotes. When you’re passing wildcards, each search phrase

must be enclosed within double quotes inside those outer single

quotes. Failing to do this will likely cause your searches to return no

matches.

CONTAINS also supports the Boolean operators AND, OR, and NOT. Here are a

couple examples:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE CONTAINS(note_text, 'safe AND handsaw');

Output

Click here to view code image

note_id note_text

----------- -----------------------------------------------

112 Customer complaint: Circular hole in safe floor

can apparently be easily cut with handsaw.

Analysis

'safe AND handsaw' means “match only rows that contain both safe and

handsaw.”

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE CONTAINS(note_text, 'rabbit AND NOT food');

Output

Click here to view code image

note_id note_text

----------- -------------------------------------------

104 Quantity varies, sold by the sack load. All

guaranteed to be bright and orange, and

suitable for use as rabbit bait.

Analysis

'rabbit AND NOT food' means “match only rows that contain the word

rabbit and do not contain the word food.”

When searching through extremely long text, you have a greater likelihood of

matches being found if search terms are near each other in the saved data. A

simple AND search matches terms anywhere in the text, but NEAR can be used to

instruct the full-text search engine to only match terms when they are close

together. Here is an example:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE CONTAINS(note_text, 'detonate NEAR quickly');

Output

Click here to view code image

note_id note_text

----------- -----------------------------------------------

105 Included fuses are short and have been known to

detonate too quickly for some customers. Longer

fuses are available (item FU1) and should be

recommended.

Analysis

'detonate NEAR quickly' means “match only rows that contain the

words detonate and quickly near each other.”

Sometimes you may want to match a word that is part of the same family (based

on the same stem). For example, if you were searching for vary, you’d also

want to match varies. Obviously, a wildcard of vary* could not help here,

and using var* would likely match too many false positives. This is where

inflectional matching helps. Here is an example:

Input

Click here to view code image

SELECT note_id, note_text

FROM productnotes

WHERE CONTAINS(note_text, 'FORMSOF(INFLECTIONAL, vary)');

Output

Click here to view code image

note_id note_text

----------- ------------------------------------------------

104 Quantity varies, sold by the sack load. All

guaranteed to be bright and orange, and suitable

for use as rabbit bait.

Analysis

'FORMSOF(INFLECTIONAL, vary)' instructs the full-text engine to look

for any words that share the same stem as the specified word (in this case,

vary). As such, the row containing the word varies was matched and

retrieved.

Note: THESAURUS Searches

FORMSOF() also supports THESAURUS searches, where words

can match synonyms. To use this functionality, you must first

populate an XML thesaurus file with words and their synonyms.

Tip: Mixing Search Types

So as to keep them simple and clear, the examples here use

wildcards, Boolean operators, proximity searches, or inflectional

searches. In truth, you can mix and match any of these as needed.

Ranking Search Results

When you perform a full-text search, the full-text engine uses sophisticated

algorithms to attempt to locate what you are looking for. It can also assign a rank

value to the match—the closer the match, the higher the assigned rank.

Ranks are accessed via ranking functions, FULLTEXT searches are ranked using

the function FULLTEXTTABLE(), and CONTAINS searches are ranked using

function CONTAINSTABLE(). Both of these functions are used the same way,

and both accept search patterns (the same search patterns already explained in

this lesson).

Here is an example:

Input

Click here to view code image

SELECT f.rank, note_id, note_text

FROM productnotes,

FREETEXTTABLE(productnotes, note_text, 'rabbit food') f

WHERE productnotes.note_id=f.[key]

ORDER BY rank DESC;

Output

Click here to view code image

rank note_id note_text

---------- ---------- -------------------------------------

412 110 Customer complaint: rabbit has been

able to detect trap, food apparently

less effective now.

62 104 Quantity varies, sold by the sack

load. All guaranteed to be bright and

orange, and suitable for use as

rabbit bait.

Analysis

This example performs a FREETEXT-type search, but instead of filtering using

the WHERE clause, it uses the FREETEXTTABLE() function and provides a

search pattern instructing the full-text engine to match any rows that contain

words meaning rabbit and food. FREETEXTTABLE() returns a table that is

given an alias of f (to be able to refer to it in column selections and the join).

This table contains a column named key, which matches the primary key of the

table that was indexed (productnotes in this example), and a column named

rank, which is the rank value assigned. The first row has a rank of 412 because

it is a better match (it actually contains both search words), whereas the second

row has a lower rank of 62 because it was a lesser match.

This technique can be used with both FULLTEXT and CONTAINS matches, but

if you are performing a CONTAINS match, the function CONTAINSTABLE()

should be used instead.

Note: Assigning Search Term Weights

The rankings assigned in this example assume that all words are

equally important and relevant. If this is not the case, and some

words are more important than others, then the ISABOUT()

function can be used to assign weight values to specific words. The

full-text search engine will then use these weight values when

determining rankings.

Summary

In this lesson, you learned why full-text searching is used, and how to use the T-

SQL FREETEXT() and CONTAINS() functions to perform these searches.

You also learned how to use Boolean operators, wildcards, proximity searches,

inflectional matches, and search rankings.

Lesson 18. Inserting Data

In this lesson, you learn how to insert data into tables using the SQL INSERT

statement.

Understanding Data Insertion

SELECT is undoubtedly the most frequently used SQL statement (which is why

the past 14 lessons were dedicated to it). But there are three other frequently

used SQL statements you should learn. The first one is INSERT. (You’ll get to

the other two in the next lesson.)

As its name suggests, INSERT is used to insert (add) rows to a database table.

INSERT can be used in several ways:

To insert a single complete row

To insert a single partial row

To insert multiple rows

To insert the results of a query

You’ll now look at examples of each of these.

Tip: INSERT and System Security

Use of the INSERT statement can be disabled per table or per user

using SQL Server security, as is explained in Lesson 29, “Managing

Security.”

Inserting Complete Rows

The simplest way to insert data into a table is to use the basic INSERT syntax,

which requires that you specify the table name and the values to be inserted into

the new row. Here is an example of this (don’t actually try this example because

it will fail):

Input

INSERT INTO Customers

VALUES(10006,

'Pep E. LaPew',

'100 Main Street',

'Los Angeles',

'CA',

'90046',

'USA',

NULL,

NULL);

Note: No Output

INSERT statements usually generate no output.

Analysis

The preceding example inserts a new customer into the customers table. The

data to be stored in each table column is specified in the VALUES clause, and a

value must be provided for every column. If a column has no value (for example,

the cust_contact and cust_email columns), the NULL value should be

used (assuming the table allows no value to be specified for that column). The

columns must be populated in the order in which they appear in the table

definition.

So why would this INSERT statement fail as is? cust_id is an identity field.

This means that values are assigned automatically by SQL Server, and each time

a new row is added, the next unused number in sequence is automatically used

and saved. By default, identity fields do not allow you to explicitly specify

values; SQL Server gets to do this itself.

Tip: Manually Specifying Identity Field Values

If you really do need to manually specify the value of an identity

field, you can do so if you first issue the statement SET

IDENTITY_INSERT = ON. Assuming that the value specified is

valid (and not already in use), subsequent inserts will auto-

increment using this new value.

Even when you’re not using identity fields, this previous form of INSERT is not

at all safe and should generally be avoided at all costs. This type of statement is

highly dependent on the order in which the columns are defined in the table. It

also depends on information about that order being readily available. Even if it is

available, there is no guarantee the columns will be in the exact same order the

next time the table is reconstructed. Therefore, writing SQL statements that

depend on specific column ordering is very unsafe. If you do so, something will

inevitably break at some point.

The safer (and, unfortunately, more cumbersome) way to write the INSERT

statement is as follows:

Input

Click here to view code image

INSERT INTO customers(cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country,

cust_contact,

cust_email)

VALUES('Pep E. LaPew',

'100 Main Street',

'Los Angeles',

'CA',

'90046',

'USA',

NULL,

NULL);

Analysis

This example does the exact same thing as the previous INSERT statement, but

this time the column names are explicitly stated in parentheses after the table

name. When the row is inserted, SQL Server matches each item in the columns

list with the appropriate value in the VALUES list. The first entry in VALUES

corresponds to the first specified column name. The second value corresponds to

the second column name, and so on.

Because column names are provided, the VALUES must match the specified

column names in the order in which they are specified, and not necessarily in the

order the columns appear in the actual table. The advantage of this is that, even

if the table layout changes, the INSERT statement will still work correctly.

You’ll notice that cust_id was not specified; unneeded columns can simply be

omitted from both the columns list and the VALUES list.

The following INSERT statement populates all the row columns (just as before),

but it does so in a different order. Because the column names are specified, the

insertion will work correctly:

Input

Click here to view code image

INSERT INTO customers(cust_name,

cust_contact,

cust_email,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country)

VALUES('Pep E. LaPew',

NULL,

'100 Main Street',

'Los Angeles',

'CA',

'90046',

'USA');

Tip: Always Use a Columns List

As a rule, never use INSERT without explicitly specifying the

column list. This greatly increases the probability that your SQL

will continue to function in the event that table changes occur.

Caution: Use VALUES Carefully

Regardless of the INSERT syntax being used, the correct number of

VALUES must be specified. If no column names are provided, a

value must be present for every table column. If column names are

provided, a value must be present for each listed column. If none is

present, an error message will be generated, and the row will not be

inserted.

Using this syntax, you can also omit columns. This means you only provide

values for some columns, but not for others. (You’ve actually already seen an

example of this; cust_id was omitted when column names were explicitly

listed.)

Caution: Omitting Columns

You may omit columns from an INSERT operation if the table

definition so allows. One of the following conditions must exist:

The column is defined as allowing NULL values (no value at all).

A default value is specified in the table definition. This means

the default value will be used if no value is specified.

If you omit a value from a table that does not allow NULL values

and does not have a default, SQL Server generates an error message,

and the row is not inserted.

Tip: INTO Is Optional

In T-SQL, the keyword INTO is optional, so INSERT INTO

customers can be shorted to INSERT customers. In practice,

so as to ensure maximum portability, INTO should always be

specified.

Inserting Multiple Rows

INSERT inserts a single row into a table. But what if you need to insert multiple

rows? The basic INSERT statement only inserts a single row at a time, so you

would need to use multiple INSERT statements. You could possibly submit

them all at once, each terminated by a semicolon, like this:

Input

Click here to view code image

INSERT INTO customers(cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country)

VALUES('Pep E. LaPew',

'100 Main Street',

'Los Angeles',

'CA',

'90046',

'USA');

INSERT INTO customers(cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country)

VALUES('M. Martian',

'42 Galaxy Way',

'New York',

'NY',

'11213',

'USA');

Note: One Set of VALUES Only

Unlike some other DBMSs, SQL Server does not support multiple

VALUES clauses for a single INSERT statement.

Inserting Retrieved Data

INSERT is generally used to add a row to a table using specified values.

Another form of INSERT can be used to insert the result of a SELECT statement

into a table. This is known as INSERT SELECT, and, as its name suggests, it is

made up of an INSERT statement and a SELECT statement.

Suppose you want to merge a list of customers from another table into your

customers table. Instead of reading one row at a time and inserting it with

INSERT, you can do the following:

Note: Instructions Needed for the Next Example

The following example imports data from a table named custnew

into the customers table. To try this example, create and populate

the custnew table first. The format of the custnew table should

be the same as the customers table described in Appendix B,

“The Example Tables.” When populating custnew, be sure not to

use cust_id values that were already used in customers (the

subsequent INSERT operation will fail if primary key values are

duplicated). Alternatively, just omit that column and have SQL

Server generate new values during the import process.

Input

Click here to view code image

INSERT INTO customers(cust_contact,

cust_email,

cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country)

SELECT cust_contact,

cust_email,

cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country

FROM custnew;

Analysis

This example uses INSERT SELECT to import all the data from custnew into

customers. Instead of listing the VALUES to be inserted, the SELECT

statement retrieves them from custnew. Each column in the SELECT

corresponds to a column in the specified columns list. How many rows will this

statement insert? That depends on how many rows are in the custnew table. If

the table is empty, no rows will be inserted (and no error will be generated

because the operation is still valid). If the table does, in fact, contain data, all that

data is inserted into customers.

Tip: Column Names in INSERT SELECT

This example uses the same column names in both the INSERT and

SELECT statements for simplicity’s sake. But there is no

requirement that the column names match. In fact, SQL Server does

not even pay attention to the column names returned by the

SELECT. Rather, the column position is used, so the first column in

the SELECT (regardless of its name) will be used to populate the

first specified table column, and so on. This is very useful when you

are importing data from tables that use different column names.

The SELECT statement used in an INSERT SELECT can include a WHERE

clause to filter the data to be inserted.

Another way to insert retrieved data is to use SELECT INTO. This variation to

SELECT allows you to specify a destination table that will be populated with the

results of a SELECT statement.

Input

SELECT cust_contact,

cust_email,

cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country

INTO customersExport

FROM customers;

Analysis

When used, the INTO clause must come after the column list and before the

FROM clause. INTO specifies the name of a table to be created, and this table

name must not already exist (or an error will be generated). After the statement

has been executed, the newly created table will contain the rows retrieved by the

SELECT statement.

SELECT INTO is very useful when you’re trying to create a single table that

contains rows retrieved from multiple tables.

Tip: INSERT SELECT versus SELECT INTO

You can think of INSERT SELECT as being an import operation,

and SELECT INTO as being an export operation.

Note: More Examples

Looking for more examples of INSERT use? See the example table

population scripts (described in Appendix A) used to create the

example tables used in this book.

Reporting On Inserted Data

Note: SQL Server 2016 Only

This section covers functionality that was introduced in SQL Server 2016

and is not available in earlier versions of SQL Server.

When rows are inserted into a table, especially when inserting multiple rows

using INSERT SELECT, you may want a list of exactly what was inserted.

SQL Server can return a list of changes to you if you use the INSERT

statement’s OUTPUT clause. Here’s an example:

Input

Click here to view code image

INSERT INTO customers(cust_contact,

cust_email,

cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country)

OUTPUT INSERTED.*

SELECT cust_contact,

cust_email,

cust_name,

cust_address,

cust_city,

cust_state,

cust_zip,

cust_country

FROM custnew;

Analysis

This INSERT statement is the same as the one used previously, with one change,

the OUTPUT clause. OUTOUT tells SQL Server to return a list of changes, and

the word OUTPUT must be followed by what it is you want returned. Here we

are returning everything that is in the INSERTED table, or INSERTED.*. What

is the INSERTED table? It is a built-in virtual table that contains information

about rows affected by the most recent INSERT statement. * obviously returns

all rows from INSERTED, and we could have returned INSERTED.cust_id

to return just the cust_id of all inserted rows.

Note: More On INSERTED

We will revisit the INSERTED table in Lesson 25, “Using

Triggers.”

Summary

In this lesson, you learned how to use INSERT to insert rows into a database

table. You learned several other ways to use INSERT, and why explicit column

specification is preferred. You also learned how to use INSERT SELECT to

import rows from another table, and SELECT INTO to export rows to a new

table. In the next lesson, you learn how to use UPDATE and DELETE to further

manipulate table data.

Lesson 19. Updating and Deleting Data

In this lesson, you learn how to use the UPDATE and DELETE statements to

enable you to further manipulate your table data.

Updating Data

To update (modify) data in a table, you use the UPDATE statement. UPDATE can

be used in two ways:

To update specific rows in a table

To update all rows in a table

Caution: Don’t Omit the WHERE Clause

Special care must be exercised when using UPDATE because it is all

too easy to mistakenly update every row in your table. Be sure to

read this entire section on UPDATE before using this statement.

Tip: UPDATE and Security

Use of the UPDATE statement can be restricted and controlled.

More on this in Lesson 29, “Managing Security.”

The UPDATE statement is very easy to use—some would say too easy. The basic

format of an UPDATE statement is made up of three parts:

The table to be updated

The column names and their new values

The filter condition that determines which rows should be updated

Let’s take a look at a simple example. Customer 10005 now has an email

address, and so his record needs updating. The following statement performs this

update:

Input

Click here to view code image

UPDATE customers

SET cust_email = 'elmer@fudd.com'

WHERE cust_id = 10005;

The UPDATE statement always begins with the name of the table being updated.

In this example, it is the customers table. The SET command is then used to

assign the new value to a column. As used here, the SET clause sets the

cust_email column to the specified value:

Click here to view code image

SET cust_email = 'elmer@fudd.com'

The UPDATE statement finishes with a WHERE clause that tells SQL Server

which row to update. Without a WHERE clause, SQL Server would update all the

rows in the customers table with this new email address—definitely not the

desired effect.

Updating multiple columns requires a slightly different syntax:

Input

Click here to view code image

UPDATE customers

SET cust_name = 'The Fudds',

cust_email = 'elmer@fudd.com'

WHERE cust_id = 10005;

When updating multiple columns, you use only a single SET command and

separate each column = value pair by a comma. (No comma is specified

after the last column.) In this example, the columns cust_name and

cust_email will both be updated for customer 10005.

Tip: Using Subqueries in an UPDATE Statement

Subqueries may be used in UPDATE statements, enabling you to

update columns with data retrieved with a SELECT statement. Refer

back to Lesson 13, “Working with Subqueries,” for more

information on subqueries and their uses.

To delete a column’s value, you can set it to NULL (assuming the table is defined

to allow NULL values). You can do this as follows:

Input

UPDATE customers

SET cust_email = NULL

WHERE cust_id = 10005;

Here, the NULL keyword is used to save no value to the cust_email column.

Deleting Data

To delete (remove) data from a table, you use the DELETE statement. DELETE

can be used in two ways:

To delete specific rows from a table

To delete all rows from a table

Caution: Don’t Omit the WHERE Clause

Special care must be exercised when using DELETE because it is all

too easy to mistakenly delete every row from your table. Be sure to

read this entire section on DELETE before using this statement.

Tip: DELETE and Security

Use of the DELETE statement can be restricted and controlled.

More on this in Lesson 29.

I already stated that UPDATE is very easy to use. The good (and bad) news is

that DELETE is even easier to use.

The following statement deletes a single row from the customers table:

Input

DELETE FROM customers

WHERE cust_id = 10006;

This statement should be self-explanatory. DELETE FROM requires that you

specify the name of the table from which the data is to be deleted. The WHERE

clause filters which rows are to be deleted. In this example, only customer

10006 will be deleted. If the WHERE clause were omitted, this statement would

have deleted every customer in the table.

DELETE takes no column names or wildcard characters. DELETE deletes entire

rows, not columns. To delete specific columns, use an UPDATE statement (as

shown earlier in this lesson).

Note: Table Contents, Not Tables

The DELETE statement deletes rows from tables, even all rows

from tables. But DELETE never deletes the table itself.

Reporting On Updated And Deleted Data

Note: SQL Server 2016 Only

This section covers functionality that was introduced in SQL Server 2016

and is not available in earlier versions of SQL Server.

In Lesson 18, “Inserting Data”, you saw how OUTPUT can be used to obtain

details about rows inserted into a table. OUTPUT can also be used with UPDATE

and DELETE statements in much the same way. Here’s an example:

Input

Click here to view code image

UPDATE customers

SET cust_state = Upper(cust_state)

OUTPUT DELETED.*;

Analysis

This UPDATE statement updates every row in the customers table, setting the

cust_state column to upper case (just in case someone entered it

incorrectly). OUTPUT DELETED.* tells SQL Server to return every row that

was updated. DELETED is a built-in virtual table, much like the INSERTED

table seen in Lesson 18, and it is used the same way.

OUTPUT is used the same way for both UPDATE and DELETE statements.

Note: No UPDATED

There is no UPDATED virtual table, just INSERTED which is used

for INSERT statements, and DELETED which is used for both

UPDATE and DELETE statements.

Note: More On DELETED

We revisit the DELETED table in Lesson 25, “Using Triggers.”

Guidelines for Updating and Deleting Data

The UPDATE and DELETE statements used in the previous sections all have

WHERE clauses, and there is a very good reason for this. If you omit the WHERE

clause, the UPDATE or DELETE is applied to every row in the table. In other

words, if you execute an UPDATE without a WHERE clause, every row in the

table is updated with the new values. Similarly, if you execute DELETE without

a WHERE clause, all the contents of the table are deleted.

Here are some best practices that many SQL programmers follow:

Never execute an UPDATE or a DELETE without a WHERE clause, unless

you really do intend to update and delete every row.

Make sure every table has a primary key (refer back to Lesson 14,

“Joining Tables,” if you have forgotten what this is), and use it as the

WHERE clause whenever possible. (You may specify individual primary

keys, multiple values, or value ranges.)

Before you use a WHERE clause with an UPDATE or a DELETE, first test it

with a SELECT to make sure it is filtering the right records—it is far too

easy to write incorrect WHERE clauses.

Use database-enforced referential integrity (refer back to Lesson 14 for

this one, too) so that SQL Server will not allow the deletion of rows that

have data in other tables related to them.

Caution: Use with Caution

The bottom line is that SQL Server has no Undo button. Be very

careful using UPDATE and DELETE; otherwise, you’ll find yourself

updating and deleting the wrong data.

Summary

In this lesson, you learned how to use the UPDATE and DELETE statements to

manipulate the data in your tables. You learned the syntax for each of these

statements, as well as the inherent dangers they expose. You also learned why

WHERE clauses are so important in UPDATE and DELETE statements, and you

were given guidelines to follow to help ensure your data does not get damaged

inadvertently.

Lesson 20. Creating and Manipulating Tables

In this lesson, you learn the basics of table creation, alteration, and deletion.

Creating Tables

T-SQL statements are not used just for table data manipulation. Indeed, T-SQL

can be used to perform all database and table operations, including the creation

and manipulation of tables themselves.

There are generally two ways to create database tables:

Using an administration tool (like Microsoft SQL Server Management

Studio) to create and manage database tables interactively

Using T-SQL statements to manipulate tables directly

To create tables programmatically, you use the CREATE TABLE SQL statement.

It is worth noting that when you use interactive tools, you are actually using T-

SQL statements. Instead of you writing these statements, however, the interface

generates and executes the T-SQL seamlessly for you (the same is true for

changes to existing tables).

Note: Additional Examples

For additional examples of table-creation scripts, see the code used

to create the sample tables used in this book.

Basic Table Creation

To create a table using CREATE TABLE, you must specify the following

information:

The name of the new table, after the keywords CREATE TABLE

The name and definition of the table columns, separated by commas

The CREATE TABLE statement may also include other keywords and options,

but at a minimum you need the table name and column details. The following T-

SQL statement creates the customers table used throughout this book:

Input

Click here to view code image

CREATE TABLE customers

(

cust_id INT NOT NULL IDENTITY(1,1),

cust_name NCHAR(50) NOT NULL ,

cust_address NCHAR(50) NULL ,

cust_city NCHAR(50) NULL ,

cust_state NCHAR(5) NULL ,

cust_zip NCHAR(10) NULL ,

cust_country NCHAR(50) NULL ,

cust_contact NCHAR(50) NULL ,

cust_email NCHAR(255) NULL ,

PRIMARY KEY (cust_id)

);

Analysis

As you can see in the preceding statement, the table name is specified

immediately following the CREATE TABLE keywords. The actual table

definition (all the columns) is enclosed within parentheses. The columns

themselves are separated by commas. This particular table is made up of nine

columns. Each column definition starts with the column name (which must be

unique within the table), followed by the column’s datatype. (Refer to Lesson 1,

“Understanding SQL,” for an explanation of datatypes. In addition, Appendix C,

“T-SQL Server Datatypes,” lists the datatypes supported by SQL Server.) The

table’s primary key may be specified at table-creation time using the PRIMARY

KEY keywords; here, column cust_id is specified as the primary key column.

The entire statement is terminated with a semicolon after the closing parenthesis.

(Ignore the IDENTITY keyword for now; we’ll come back to that later.)

Tip: Statement Formatting

As you will recall, white space is ignored in T-SQL statements.

Statements can be typed on one long line or broken up over many

lines. It makes no difference at all. This enables you to format your

SQL as best suits you. The preceding CREATE TABLE statement is

a good example of T-SQL statement formatting—the code is

specified over multiple lines, with the column definitions indented

for easier reading and editing. Formatting your T-SQL in this way is

entirely optional, but highly recommended.

Tip: Handling Existing Tables

When you create a new table, the table name specified must not

exist; otherwise, you’ll generate an error. To prevent accidental

overwriting, SQL Server requires that you first manually remove a

table (see later sections for details) and then re-create it, rather than

just overwrite it.

Working with NULL Values

Back in Lesson 6, “Filtering Data,” you learned that a NULL value is no value or

the lack of a value. A column that allows NULL values also allows rows to be

inserted with no value at all in that column. A column that does not allow NULL

values does not accept rows with no value—in other words, that column will

always be required when rows are inserted or updated.

Every table column is either a NULL column or a NOT NULL column, and that

state is specified in the table definition at creation time. Take a look at the

following example:

Input

Click here to view code image

CREATE TABLE orders

(

order_num INT NOT NULL IDENTITY(1,1),

order_date DATETIME NOT NULL ,

cust_id INT NOT NULL ,

PRIMARY KEY (order_num)

);

Analysis

This statement creates the orders table used throughout this book. orders

contains three columns: the order number, order date, and customer ID. All three

columns are required, so each contains the keyword NOT NULL. This will

prevent the insertion of columns with no value. If someone tries to insert no

value, an error will be returned, and the insertion will fail.

Caution: Understanding NULL

Don’t confuse NULL values with empty strings. A NULL value is

the lack of a value; it is not an empty string. If you were to specify

'e' (two single quotes with nothing in between them), that would

be allowed in a NOT NULL column. An empty string is a valid

value; it is not no value. NULL values are specified with the

keyword NULL, not with an empty string.

Primary Keys Revisited

As already explained, primary key values must be unique. That is, every row in a

table must have a unique primary key value. If a single column is used for the

primary key, it must be unique; if multiple columns are used, the combination of

them must be unique.

The CREATE TABLE examples seen thus far use a single column as the primary

key. The primary key is thus defined using a statement such as

PRIMARY KEY (vend_id)

To create a primary key made up of multiple columns, simply specify the

column names as a comma-delimited list, as shown in this example:

Input

Click here to view code image

CREATE TABLE orderitems

(

order_num INT NOT NULL ,

order_item INT NOT NULL ,

prod_id NCHAR(10) NOT NULL ,

quantity INT NOT NULL ,

item_price MONEY NOT NULL ,

PRIMARY KEY (order_num, order_item)

);

The orderitems table contains the order specifics for each order in the

orders table. There may be multiple items per order, but each order will only

ever have one first item, one second item, and so on. As such, the combination

of order number (column order_num) and order item (column order_item)

is unique, and thus suitable to be the primary key, which is defined as follows:

Click here to view code image

PRIMARY KEY (order_num, order_item)

Primary keys may be defined at table-creation time (as seen here) or after table

creation (as will be discussed later in this lesson).

Tip: Primary Keys and NULL Values

Back in Lesson 1, you learned that primary keys are columns whose

values uniquely identify every row in a table. Only columns that do

not allow NULL values can be used in primary keys. Columns that

allow no value at all cannot be used as unique identifiers.

Using IDENTITY

Let’s take a look at the customers and orders tables again. Customers in

the customers table are uniquely identified by column cust_id, a unique

number for each and every customer. Similarly, orders in the orders table each

have a unique order number stored in the column order_num.

These numbers have no special significance, other than the fact that they are

unique. When a new customer or order is added, a new customer ID or order

number is needed. The numbers can be anything, as long as they are unique.

Obviously, the simplest number to use would be whatever comes next, whatever

is one higher than the current highest number. For example, if the highest

cust_id is 10005, the next customer inserted into the table could have a

cust_id of 10006.

Simple, right? Well, not really. How would you determine the next number to be

used? You could, of course, use a SELECT statement to get the highest number,

using the Max() function introduced in Lesson 11, “Summarizing Data,” and

then add 1 to it. But that would not be safe (you’d need to find a way to ensure

that no one else inserted a row in between the time that you performed the

SELECT and the INSERT, a legitimate possibility in multiuser applications).

Nor would it be efficient (performing additional T-SQL operations is never

ideal).

And that’s where IDENTITY comes in. Look at the following line (part of the

CREATE TABLE statement used to create the customers table):

Click here to view code image

cust_id INT NOT NULL IDENTITY(1,1),

IDENTITY tells SQL Server that this column is to be automatically incremented

each time a row is added. Each time an INSERT operation is performed, SQL

Server automatically increments the column, assigning it the next available

value. This way, each row is assigned a unique cust_id, which is then used as

the primary key value.

IDENTITY needs to know the number to start from (known as the seed) and the

increment to be used each time a new value is generated. IDENTITY(1,1)

means “start with a seed of 1 and increment by 1 to generate each new number.”

To start with a seed of 100 and increment by 10, you could use

IDENTITY(100,10). If the seed and increment values are not provided,

(1,1) is used by default.

Only one IDENTITY column is allowed per table, and IDENTITY columns are

usually used as primary keys.

Note: Overriding IDENTITY

Need to use a specific value in a column designated as IDENTITY?

You can—but first you’ll need to SET IDENTITY_INSERT to

ON, as mentioned in Lesson 18, “Inserting Data.” (See the table-

population scripts used in this book for examples of this.)

Tip: Determining the IDENTITY Value

One downside of having SQL Server generate (via IDENTITY)

primary keys for you is that you don’t know what those values are.

Consider this scenario: You are adding a new order. This requires

creating a single row in the orders table and then a row for each

item ordered in the orderitems table. The order_num is stored

along with the order details in orderitems. This is how the

orders and orderitems table are related to each other. And

that obviously requires that you know the generated order_num

after the orders row was inserted and before the orderitems

rows are inserted.

So how could you obtain this value when an IDENTITY column is

used? By referring to the special @@IDENTITY system function,

like this:

SELECT @@IDENTITY AS newId;

This returns the last generated IDENTITY value as newId, which

you can then use in subsequent T-SQL statements.

Specifying Default Values

SQL Server enables you to specify default values to be used if no value is

specified when a row is inserted. Default values are specified using the

DEFAULT keyword in the column definitions in the CREATE TABLE statement.

Look at the following example:

Input

Click here to view code image

CREATE TABLE orderitems

(

order_num INT NOT NULL ,

order_item INT NOT NULL ,

prod_id NCHAR(10) NOT NULL ,

quantity INT NOT NULL DEFAULT 1,

item_price MONEY NOT NULL ,

PRIMARY KEY (order_num, order_item)

);

Analysis

This statement creates the orderitems table, which contains the individual

items that make up an order. (The order itself is stored in the orders table.)

The quantity column contains the quantity for each item in an order. In this

example, adding the text DEFAULT 1 to the column description instructs SQL

Server to use a quantity of 1 if no quantity is specified.

The previous example used a fixed value as the specified DEFAULT. You can

also use T-SQL functions as default values, as shown in this example:

Input

Click here to view code image

CREATE TABLE orders

(

order_num INT NOT NULL IDENTITY(1,1),

order_date DATETIME NOT NULL DEFAULT GetDate(),

cust_id INT NOT NULL ,

PRIMARY KEY (order_num)

);

Analysis

Orders are stored in the orders table, and the order date is stored in the

order_date column. The default value for this column is GetDate(), a T-

SQL function that returns the current system date. This way, if an explicit

order_date value is not specified when a row is inserted, the current date will

be used automatically.

By the way, despite its name, GetDate() actually returns the current system

date and time. If you want to see exactly what GetDate() returns, try the

following simple SELECT statement:

Input

SELECT GetDate();

Tip: Using DEFAULT Instead of NULL Values

Many database developers use DEFAULT values instead of NULL

columns, especially in columns that will be used in calculations or

data groupings.

Updating Tables

To update table definitions, you use the ALTER TABLE statement. But, ideally,

tables should never be altered after they contain data. You should spend

sufficient time anticipating future needs during the table-design process so

extensive changes are not required later on.

To change a table using ALTER TABLE, you must specify the following

information:

The name of the table to be altered after the keywords ALTER TABLE.

(The table must exist, or an error is generated.)

The list of changes to be made.

The following example adds a column to a table:

Input

ALTER TABLE vendors

ADD vend_phone CHAR(20);

Analysis

This statement adds a column named vend_phone to the vendors table. The

datatype must be specified.

To remove this newly added column, you can do the following:

Input

ALTER TABLE vendors

DROP COLUMN vend_phone;

One common use for ALTER TABLE is to define foreign keys. The following is

the code used to define the foreign keys used by the tables in this book:

Input

Click here to view code image

ALTER TABLE orderitems

ADD CONSTRAINT fk_orderitems_orders FOREIGN KEY (order_num)

REFERENCES orders (order_num);

ALTER TABLE orderitems

ADD CONSTRAINT fk_orderitems_products FOREIGN KEY (prod_id)

REFERENCES products (prod_id);

ALTER TABLE orders

ADD CONSTRAINT fk_orders_customers FOREIGN KEY (cust_id)

REFERENCES customers (cust_id);

ALTER TABLE products

ADD CONSTRAINT fk_products_vendors FOREIGN KEY (vend_id)

REFERENCES vendors (vend_id);

ALTER TABLE productnotes

ADD CONSTRAINT fk_productnotes_products FOREIGN KEY (prod_id)

REFERENCES products (prod_id);

Analysis

Here, five ALTER TABLE statements are used because five different tables are

being altered. Each of the statements defines a foreign key. If we have needed

multiple foreign keys on a single table, those could all have been defined using a

single ALTER TABLE statement.

Complex table structure changes may require a manual move process involving

these steps:

1. Create a new table with the new column layout.

2. Use the INSERT SELECT or SELECT INTO statement (see Lesson 18,

“Inserting Data,” for details of this statement) to copy the data from the old

table to the new table. Use conversion functions and calculated fields, if

needed.

3. Verify that the new table contains the desired data.

4. Rename the old table (or delete it, if you are really brave).

5. Rename the new table with the name previously used by the old table.

6. Re-create any triggers, stored procedures, indexes, and foreign keys as

needed.

Caution: Use ALTER TABLE Carefully

Use ALTER TABLE with extreme caution, and be sure you have a

complete set of backups (both schema and data) before proceeding.

Database table changes cannot be undone—and if you add columns

you don’t need, you might not be able to remove them. Similarly, if

you drop a column that you do need, you might lose all the data in

that column.

Deleting Tables

Deleting tables (actually removing the entire table, not just the contents) is very

easy—arguably too easy. Tables are deleted using the DROP TABLE statement:

Input

DROP TABLE customers2;

Analysis

This statement deletes the customers2 table (assuming it exists). There is no

confirmation, nor is there an undo—executing the statement will permanently

remove the table.

Renaming Tables

There is no T-SQL statement for renaming tables, but a SQL Server–provided

stored procedure named sp_rename can be used to accomplish this:

Input

Click here to view code image

EXEC sp_rename 'customers2', 'customers';

Analysis

sp_rename can be used to rename all sorts of objects, including tables. This

example renames table customers2 to customers.

Summary

In this lesson, you learned several new SQL statements. CREATE TABLE is

used to create new tables, ALTER TABLE is used to change table columns (or

other objects such as constraints and indexes), and DROP TABLE is used to

completely delete a table. These statements should be used with extreme caution,

and only after backups have been made. You also learned about identity fields,

defining primary and foreign keys, and other important table and column

options.

Lesson 21. Using Views

In this lesson, you learn exactly what views are, how they work, and when they

should be used. You also see how views can be used to simplify some of the SQL

operations performed in earlier lessons.

Understanding Views

Views are virtual tables. Unlike tables that contain data, views simply contain

queries that dynamically retrieve data when used.

The best way to understand views is to look at an example. Back in Lesson 14,

“Joining Tables,” you used the following SELECT statement to retrieve data

from three tables:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM customers, orders, orderitems

WHERE customers.cust_id = orders.cust_id

AND orderitems.order_num = orders.order_num

AND prod_id = 'TNT2';

That query was used to retrieve the customers who had ordered a specific

product. Anyone needing this data would have to understand the table structure

as well as how to create the query and join the tables. To retrieve the same data

for another product (or for multiple products), you would have to modify the last

WHERE clause.

Now imagine that you could wrap that entire query in a virtual table called

productcustomers. You could then simply do the following to retrieve the

same data:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM productcustomers

WHERE prod_id = 'TNT2';

This is where views come into play. productcustomers is a view, and as a

view, it does not contain any actual columns or data as a table would. Instead, it

contains a SQL query—the same query used previously to join the tables

properly.

Why Use Views

You’ve already seen one use for views. Here are some other common uses:

To reuse SQL statements.

To simplify complex SQL operations. After the query is written, it can be

reused easily, without you having to know the details of the underlying

query itself.

To expose parts of a table instead of complete tables.

To secure data. Users can be given access to specific subsets of tables

instead of to entire tables.

To change data formatting and representation. Views can return data

formatted and presented differently from their underlying tables.

For the most part, after views are created, they can be used in the same way as

tables. You can perform SELECT operations, filter and sort data, join views to

other views or tables, and even add and update data (with some restrictions, as

noted later in this lesson).

The important thing to remember is, views are just that, views into data stored

elsewhere. Views contain no data themselves, so the data they return is retrieved

from other tables. When data is added or changed in those tables, the views

return that changed data.

View Rules and Restrictions

Here are some of the most common rules and restrictions governing view

creation and usage:

Like tables, views must be uniquely named. (They cannot have the name

of any other table or view.)

There is no limit to the number of views that can be created.

SQL Server views may contain no more than 1,024 columns.

To create views, you must have security access. This is usually granted by

the database administrator.

Views can be nested; that is, a view may be built using a query that

retrieves data from another view.

ORDER BY may not be used in views, but ORDER BY may be used in the

SELECT statement that retrieves data from the view.

Views cannot be indexed, nor can they have triggers or default values

associated with them.

Views can be used in conjunction with tables (for example, to create a

SELECT statement that joins a table and a view).

Using Views

So now that you know what views are (and the rules and restrictions that govern

them), let’s look at view creation:

Views are created using the CREATE VIEW statement.

To remove a view, you use the DROP statement. The syntax is simply

DROP VIEW viewname;.

To update a view, you may use the DROP statement and then the CREATE

VIEW statement again.

Using Views to Simplify Complex Joins

One of the most common uses of views is to hide complex SQL, and this often

involves joins. Look at the following statement:

Input

Click here to view code image

CREATE VIEW productcustomers AS

SELECT cust_name, cust_contact, prod_id

FROM customers, orders, orderitems

WHERE customers.cust_id = orders.cust_id

AND orderitems.order_num = orders.order_num;

CREATE statements don’t generally return results, but the Results panel should

show:

Click here to view code image

Command(s) completed successfully.

Analysis

This statement creates a view named productcustomers, which joins three

tables to return a list of all customers who have ordered any product. If you were

to SELECT * FROM productcustomers, you’d list every customer who

ordered anything.

To retrieve a list of customers who ordered product TNT2, you can do the

following:

Input

Click here to view code image

SELECT cust_name, cust_contact

FROM productcustomers

WHERE prod_id = 'TNT2';

Output

Click here to view code image

cust_nam cust_contact

-------------------- ------------

Coyote Inc. Y Lee

Yosemite Place Y Sam

Analysis

This statement retrieves specific data from the view by issuing a WHERE clause.

When SQL Server processes the request, it adds the specified WHERE clause to

any existing WHERE clauses in the view query so the data is filtered correctly.

As you can see, views can greatly simplify the use of complex SQL statements.

Using views, you can write the underlying SQL once and then reuse it as needed.

Tip: Creating Reusable Views

It is a good idea to create views that are not tied to specific data. For

example, the view created in this example returns customers for all

products, not just product TNT2 (for which the view was first

created). Expanding the scope of the view enables it to be reused,

making it even more useful. It also eliminates the need for you to

create and maintain multiple similar views.

Using Views to Reformat Retrieved Data

As mentioned previously, another common use of views is for reformatting

retrieved data. The following SELECT statement (from Lesson 9, “Creating

Calculated Fields”) returns vendor name and location in a single combined

calculated column:

Input

Click here to view code image

SELECT RTrim(vend_name) + ' (' + RTrim(vend_country) + ')'

AS vend_title

FROM vendors

ORDER BY vend_name;

Output

Click here to view code image

vend_title

--------------------------------------------------

ACME (USA)

Anvils R Us (USA)

Furball Inc. (USA)

Jet Set (England)

Jouets Et Ours (France)

LT Supplies (USA)

Now suppose that you regularly needed results in this format. Rather than

perform the concatenation each time it is needed, you can create a view and use

that instead. To turn this statement into a view, you can do the following:

Input

Click here to view code image

CREATE VIEW vendorlocations AS

SELECT RTrim(vend_name) + ' (' + RTrim(vend_country) + ')'

AS vend_title

FROM vendors;

Analysis

This statement creates a view using the exact same query as the previous

SELECT statement. To retrieve the data to create all mailing labels, simply do

the following:

Input

SELECT *

FROM vendorlocations

ORDER BY vend_title;

Output

Click here to view code image

vend_title

--------------------------------------------------

ACME (USA)

Anvils R Us (USA)

Furball Inc. (USA)

Jet Set (England)

Jouets Et Ours (France)

LT Supplies (USA)

Using Views to Filter Unwanted Data

Views are also useful for applying common WHERE clauses. For example, you

might want to define a customeremaillist view that filters out customers

without email addresses. To do this, you can use the following statement:

Input

Click here to view code image

CREATE VIEW customeremaillist AS

SELECT cust_id, cust_name, cust_email

FROM customers

WHERE cust_email IS NOT NULL;

Analysis

Obviously, when sending email to a mailing list you’d want to ignore users who

have no email address. The WHERE clause here filters out those rows that have

NULL values in the cust_email columns so they’ll not be retrieved.

View customeremaillist can now be used for data retrieval just like any

table.

Input

SELECT *

FROM customeremaillist;

Output

Click here to view code image

cust_id cust_name cust_email

----------- ------------------- --------------------

10001 Coyote Inc. ylee@coyote.com

10003 Wascals rabbit@wascally.com

10004 Yosemite Place sam@yosemite.com

10005 E Fudd elmer@fudd.com

Note: WHERE Clauses and WHERE Clauses

If you use a WHERE clause when retrieving data from the view, the

two sets of clauses (the one in the view and the one passed to it) will

be combined automatically.

Using Views with Calculated Fields

Views are exceptionally useful for simplifying the use of calculated fields. The

following is a SELECT statement introduced in Lesson 9. It retrieves the order

items for a specific order, calculating the expanded price for each item:

Input

Click here to view code image

SELECT prod_id,

quantity,

item_price,

quantity*item_price AS expanded_price

FROM orderitems

WHERE order_num = 20005;

Output

Click here to view code image

prod_id quantity item_price expanded_price

---------- ----------- --------------------- --------------

ANV01 10 5.99 59.90

ANV02 3 9.99 29.97

TNT2 5 10.00 50.00

FB 1 10.00 10.00

To turn this into a view, do the following:

Input

Click here to view code image

CREATE VIEW orderitemsexpanded AS

SELECT order_num,

prod_id,

quantity,

item_price,

quantity*item_price AS expanded_price

FROM orderitems;

To retrieve the details for order 20005 (the previous output), do the following:

Input

SELECT *

FROM orderitemsexpanded

WHERE order_num = 20005;

Output

Click here to view code image

order_num prod_id quantity item_price expanded_price

--------- ---------- ----------- ----------- --------------

20005 ANV01 10 5.99 59.90

20005 ANV02 3 9.99 29.97

20005 TNT2 5 10.00 50.00

20005 FB 1 10.00 10.00

As you can see, views are easy to create and even easier to use. Used correctly,

views can greatly simplify complex data manipulation.

Updating Views

All of the views thus far have been used with SELECT statements. But can view

data be updated? The answer is, it depends.

As a rule, yes, views are updateable (that is, you can use INSERT, UPDATE, and

DELETE on them). Updating a view updates the underlying table (the view, you

will recall, has no data of its own); if you add or remove rows from a view, you

are actually adding them to or removing them from the underlying table.

But not all views are updateable. Basically, if SQL Server is unable to correctly

ascertain the underlying data to be updated, updates (this includes inserts and

deletes) are not allowed. In practice, this means that if any of the following are

used, you’ll not be able to update the view:

Multiple base tables

Grouping (using GROUP BY and HAVING)

Joins

Subqueries

Unions

Aggregate functions, such as Min(), Count(), Sum(), and so forth

DISTINCT

Derived (calculated) columns

In other words, many of the examples used in this lesson would not be

updateable. This might sound like a serious restriction, but in reality it isn’t

because views are primarily used for data retrieval anyway.

Summary

Views are virtual tables. They do not contain data; they contain queries that

retrieve data, as needed, instead. Views provide a level of encapsulation around

SQL Server SELECT statements and can be used to simplify data manipulation

as well as to reformat or secure underlying data.

Lesson 22. Programming with T-SQL

Although T-SQL is not a general-purpose programming language, it does

support programming constructs such as variables and conditional processing.

Because these will be used in the next few lessons, this lesson introduces these T-

SQL programming concepts.

Understanding T-SQL Programming

All of the SQL statements used thus far have been single standalone statements

—SELECT statements to retrieve data, ALTER TABLE statements to make

table changes, and so on. But some data-retrieval tasks are more complex, often

involving multiple statements, conditional processing, and mid-process data

manipulation.

T-SQL is not a general-purpose programming language, but it does support some

basic programming ideas and constructs that can be used within data

manipulation processes. These are generally not used when working with simple

SQL statements, but are often used when working with stored procedures

(introduced in Lesson 23, “Working with Stored Procedures”), cursors (see

Lesson 24, “Using Cursors”), triggers (explained in Lesson 25, “Using

Triggers”), and more.

As such, it is worthwhile to briefly examine these capabilities, specifically the

following:

Using variables

Performing conditional processing

Repeating processes (looping)

Using Variables

In computer programming, a variable is a named object that stores values.

Although variable use and capabilities vary from one programming language to

the next, the basic ability to define variables and store values in them for

subsequent use is pretty universal.

T-SQL variables have specific rules and requirements:

All T-SQL variables names must start with @, local variables are prefixed

with @, and global variables (which are used extensively by SQL Server

itself and generally should not be used for your own purposes) are prefixed

with @@.

Before they can be used, T-SQL variables must be declared using the

DECLARE statement.

When a variable is declared, its datatype must be specified. (Refer to

Lesson 1, “Understanding SQL,” for an explanation of datatypes. In

addition, Appendix D, “SQL Server Datatypes,” lists the datatypes

supported by SQL Server.)

Multiple variables may be defined using individual DECLARE statements,

or they may be comma-delimited in a single DECLARE statement.

There is no way to “undeclare” variables. They remain present until they

go out of scope. For local variables, this means they’ll exist until the

process completes.

Declaring Variables

The following example demonstrates the use of DECLARE:

Input

Click here to view code image

DECLARE @age INT;

DECLARE @firstName CHAR(20), @lastName CHAR(20);

Analysis

This example declares three variables. The first DECLARE statement declares a

single variable named @age of type INT (an integer, a numeric value), and the

second declares two variables of type CHAR(20) (a 20-character-length string).

Notice that the second DECLARE statement requires a comma between the

variables being declared.

Assigning Values to Variables

When variables are first declared, they contain no values (actually, they contain

NULL). To assign values to variables, you use the SET statement, as shown here:

Input

Click here to view code image

DECLARE @age INT;

DECLARE @firstName CHAR(20), @lastName CHAR(20);

SET @lastName='Forta';

SET @firstName='Ben';

SET @age=21;

Analysis

This example declares the same three variables as seen previously, and then uses

three SET statements to assign values to those variables (my name and age,

wishfully thinking).

Tip: Assign Default Values

Many T-SQL developers find it useful to always initialize variables

with default or initial values right after they have been declared.

You can also use SELECT to assign values to variables, as shown here:

Input

SELECT @age=21;

In practice, SET is used to assign values to variables, unless the values are the

result of a SELECT operation, in which case SELECT is obviously used.

Note: SET or SELECT?

There is one important difference between using SET or SELECT to

assign variable values. SET sets only a single variable; to assign

values to multiple variables you must use multiple SET statements.

SELECT, on the other hand, can be used to assign values to multiple

variables in a single statement.

Viewing Variable Contents

When using variables, it is often necessary to inspect their contents. The simplest

way to view variable contents is to output them, and there are two ways to do

this. SELECT can be used to retrieve variable values, as shown here:

Input

Click here to view code image

DECLARE @age INT;

DECLARE @firstName CHAR(20), @lastName CHAR(20);

SET @lastName='Forta';

SET @firstName='Ben';

SET @age=21;

SELECT @lastName, @firstName, @age

Output

Click here to view code image

------------------- ------------------- -----------

Forta Ben 21

Analysis

As seen here, SELECT can return variables. However, the variable names

themselves are not returned.

Tip: Use Aliases

You can use aliases in SELECT statements returning variables by

using the AS keyword (see Lesson 9, “Creating Calculated Fields”).

T-SQL also supports the PRINT statement, which is used to display messages

along with any returned results. Here is an example:

Input

Click here to view code image

DECLARE @age INT;

DECLARE @firstName CHAR(20), @lastName CHAR(20);

SET @lastName='Forta';

SET @firstName='Ben';

SET @age=21;

PRINT @lastName + ', ' + @firstName;

PRINT @age;

Output

Forta , Ben

Analysis

PRINT simply outputs text. In this example, the first line is a string made up of

three strings, a variable, static (fixed) text, and then another variable. The second

line prints a numeric variable.

Tip: Converting Variables to Strings

What if you wanted to display the age output as Age: 21? You’d

need to concatenate the string Age: with the variable @age, but the

simple + concatenation operation used to join the strings together

would fail because @age is a number, not a string. To get around

this problem, you would need to convert @age to a string so as to

be able to concatenate it, like this:

Click here to view code image

PRINT 'Age: ' + Convert(CHAR, @age);

Note: Using the Debugger

There is another way to inspect variable contents. SQL Server

supports the use of a debugger to step through SQL code line by

line, allowing you to inspect variable contents in the process.

Unfortunately, coverage of the SQL Server Debugger is beyond the

scope of this book, but relevant documentation is included with

SQL Server itself.

Using Variables in T-SQL Statements

Now that you know how to declare, populate, and view the contents of variables,

let’s look at a practical example of how variables could be used.

Suppose you need to run two queries—one to return customer information for a

specific customer, and another to return orders placed by that customer. This

requires two SELECT statements, as shown here:

Input

SELECT cust_name, cust_email

FROM customers

WHERE cust_id = 10001;

SELECT order_num, order_date

FROM orders

WHERE cust_id = 10001

ORDER BY order_date;

Output

Click here to view code image

cust_name cust_email

------------------------------ ------------------

Coyote Inc. ylee@coyote.com

order_num order_date

----------- -----------------------

20005 2005-09-01 00:00:00.000

20009 2005-10-08 00:00:00.000

Analysis

This batch-processing example is pretty self-explanatory; two SELECT

statements are used, so two sets of results are returned.

New Term: Batch Processing

A batch is a set of SQL statements all submitted together to SQL

Server for processing.

Notice that both SELECT statements use a cust_id value in their WHERE

clauses. To run the same queries for another customer, you need to update both

WHERE clauses. Obviously, that leaves room for error; there is a possibility that

someone will update one of the WHERE clauses but not the other, which would

result in incorrect data being returned. In this simple example, there are only two

places where the code would need updating to perform a different search.

Imagine a more complex example, though, where the customer ID is used in lots

of places. Obviously, the chance of error increases with the length and

complexity of the code being used.

A better alternative to the preceding example would be to define the customer ID

only once, thus requiring only one change to perform a different search. Look at

this example:

Input

Click here to view code image

-- Define @cust_id

DECLARE @cust_id INT;

SET @cust_id = 10001;

-- Get customer name and e-mail

SELECT cust_name, cust_email

FROM customers

WHERE cust_id = @cust_id;

-- Get customer order history

SELECT order_num, order_date

FROM orders

WHERE cust_id = @cust_id

ORDER BY order_date;

Output

Click here to view code image

cust_name cust_email

------------------------------ ------------------

Coyote Inc. ylee@coyote.com

order_num order_date

----------- -----------------------

20005 2005-09-01 00:00:00.000

20009 2005-10-08 00:00:00.000

Analysis

In this example, the same two SELECT statements are used. But this time a local

variable named @cust_id is first defined and populated. The two SELECT

statements then use that variable in their WHERE clauses as WHERE cust_id

= @cust_id. When processed by SQL Server, the value in the variable

@cust_id is used to construct the final WHERE clause.

Caution: No Single Quotes When Using Variables

As explained in Lesson 6, “Filtering Data,” strings used in SQL

statements are always enclosed within single quotes. But when

using variables in a SQL statement, do not enclose single quotes

around the variable names, even when using them as strings. Single

quotes are needed when assigning values to string variables, but

they should not be used when you are actually using the variables.

Tip: Comment Your Code

You may have noticed that the preceding example contains lines of

code beginning with --. These are comments (messages included in

your SQL code that are ignored by SQL Server), and they help

explain what the code is doing. As the complexity of SQL

statements increases, it is invaluable to be able to read embedded

comments the next time someone has to understand what was done

and why.

Using Conditional Processing

Conditional processing is a way to make decisions within programming code,

performing some action based on the decision made. Like most other

programming languages, T-SQL allows developers to write code where decisions

are made at runtime, and what makes this work is the IF statement.

Let’s start with a basic example. Imagine you are writing a SQL statement that

has to process open orders (perhaps updating values, or copying rows to another

table). This SQL code would need to run regularly, but what it does might differ

based on whether today is a weekday (and thus a day when you are open for

business and processing orders) or part of the weekend (when you are not

processing orders).

Getting the current day of week is easy using T-SQL date and time functions

(which were introduced back in Lesson 10, “Using Data Manipulation

Functions”). GetDate() returns the current date and time, and DatePart()

returns a part of a date (the day, the day of week, the month, and so on). To get

the current day of week, you could use

DatePart(dw, GetDate())

The following is a simple IF statement that sets a variable to either 0 or 1,

based on whether or not today is Sunday:

Input

Click here to view code image

-- Define variables

DECLARE @open BIT

-- Open for business today?

IF DatePart(dw, GetDate()) = 1

SET @open = 0

ELSE

SET @open = 1

-- Output

SELECT @open AS OpenForBusiness

Output

OpenForBusiness

---------------

Analysis

Here, a local variable named @open is declared; it is of type BIT, which can

contain only 0 (false, off, no) or 1 (true, on, yes). The IF statement compares

the current day of week value, as returned by DatePart(dw, GetDate()),

to 1 (Sunday), and sets @open to 0 if true (today is Sunday) or 1 if false (today

is not Sunday). And then finally, @open is returned by a SELECT statement,

although in reality @open would probably be used in subsequent processing (as

opposed to simply being returned).

Note: ELSE Is Optional

The example here uses an ELSE clause to define the code to be

processed if the IF test returns FALSE. The use of ELSE is

optional, and many IF statements do not have an ELSE clause.

Of course, there is a flaw in this code because it only checks to see if today is

Sunday. As such, if today were Saturday, @open would mistakenly be set to 1.

To fix this, we need to add an OR to the IF statement:

Input

Click here to view code image

-- Define variables

DECLARE @dow INT

DECLARE @open BIT

-- Get the day of week

SET @dow = DatePart(dw, GetDate());

-- Open for business today?

IF @dow = 1 OR @dow = 7

SET @open = 0

ELSE

SET @open = 1

-- Output

SELECT @open AS OpenForBusiness

Analysis

There are two changes in this version of the SQL code. An OR operator is now

used in the IF statement so that either 1 (Sunday) or 7 (Saturday) matches the

test. In addition, instead of obtaining the current day of week right in the IF

statement, we declare a local variable named @dow (for day of week) and

populate it with the correct value. To understand the value of this change, look at

the alternative:

Click here to view code image

IF DatePart(dw, GetDate()) = 1 OR DatePart(dw, GetDate()) = 7

So as to not have to obtain the current day of week twice in the IF statement,

that code is processed earlier and the result is saved to a variable.

T-SQL supports both AND and OR operators in IF statements, as well as

parentheses, which are used to define the order of evaluation. (Refer back to

Lesson 7, “Advanced Data Filtering,” for an introduction to AND, OR, and using

parentheses to define the order of evaluation.)

Grouping Statements

As you have seen, IF is used to conditionally process whatever directly follows

it. In the previous examples, a single statement was processed if the IF or ELSE

conditions were met. But what would happen if you had to execute multiple

statements? Look at this example:

Input

Click here to view code image

-- Define variables

DECLARE @dow INT

DECLARE @open BIT, @process BIT

-- Get the day of week

SET @dow = DatePart(dw, GetDate());

-- Open for business today?

IF @dow = 1 OR @dow = 7

SET @open = 0

SET @process = 0

ELSE

SET @open = 1

SET @process = 1

Analysis

If you were to run this code, an error would be generated. Why? Because after

the IF has been processed, the first SET will either be processed (if the IF

condition was met) or ignored (if the IF test failed). But regardless, the second

SET statement will always be executed; it is not dependent on IF processing

(even though the code indentation makes it appear to be). The error is actually

caused by the ELSE statement, because SQL Server sees it as an extraneous

ELSE not tied to any IF.

To solve this problem, two new keywords are needed, BEGIN and END. Look at

this example:

Input

Click here to view code image

-- Define variables

DECLARE @dow INT

DECLARE @open BIT, @process BIT

-- Get the day of week

SET @dow = DatePart(dw, GetDate());

-- Open for business today?

IF @dow = 1 OR @dow = 7

BEGIN

SET @open = 0

SET @process = 0

END

ELSE

BEGIN

SET @open = 1

SET @process = 1

END

Analysis

Here, BEGIN and END are used to define a block of code. Now, when the IF or

ELSE is processed, the entire block enclosed between BEGIN and END will be

processed, instead of just the subsequent statement.

BEGIN and END are important statements and are not just used in conjunction

with IF, as is shown in the upcoming lessons.

Tip: Indent Your Code

The code in the previous example uses two levels of indentation—

the first to line up the code executed by IF or ELSE, and the second

to clearly define the contents of each BEGIN and END block. There

are no hard-and-fast rules dictating how indentation is to be used;

you can use the style used here, and anything else that helps you

better organize and read your code.

Using Looping

SQL statements are processed sequentially, one at a time, and each being

processed once. Like other programming languages, T-SQL supports looping,

the ability to repeat a block of code as needed. In T-SQL, looping is

accomplished using the WHILE statement.

Note: WHILE and Cursors

WHILE tends to be used most frequently in conjunction with

cursors, as explained in Lesson 24.

The following is a simple (albeit atypical and rather useless) demonstration of

how to use WHILE:

Input

DECLARE @counter INT

SET @counter=1

WHILE @counter <= 10

BEGIN

PRINT @counter

SET @counter=@counter+1

END

Output

Analysis

This example defines a local variable named @counter and initializes it with a

value of 1. The WHILE loop tests to see if @counter is less than or equal to

10, and as long as that condition is true, the number is displayed using PRINT

and then incremented.

Note: WHILE and BEGIN/END

Just like IF, WHILE repeats only the single statement that follows

it. To repeat multiple lines of code, use BEGIN and END to delimit

that code block, as shown in the previous example.

Two other statements are frequently used in conjunction with WHILE:

BREAK immediately exits the current WHILE loop (or IF).

CONTINUE restarts processing at the top of the loop.

Summary

T-SQL supports basic programming constructs, including variables, conditional

processing, and looping. In and of themselves, these are not that useful, but they

are very important when used in conjunction with other SQL Server features, as

you will see in the upcoming lessons.

Lesson 23. Working with Stored Procedures

In this lesson, you learn what stored procedures are, why they are used, and how

they are used. You also look at the basic syntax for creating and using them.

Understanding Stored Procedures

Most of the SQL statements we’ve used thus far are simple in that they use a

single statement against one or more tables. Not all operations are that simple—

often, multiple statements are needed to perform a complete operation. For

example, consider the following scenario:

To process an order, checks must be made to ensure that items are in stock.

If items are in stock, they need to be reserved so they are not sold to

anyone else, and the available quantity must be reduced to reflect the

correct amount in stock.

Any items not in stock need to be ordered; this requires some interaction

with the vendor.

The customer needs to be notified as to which items are in stock (and can

be shipped immediately) and which are backordered.

This is obviously not a complete example, and it is even beyond the scope of the

sample tables we have been using in this book, but it will suffice to help make a

point. Performing this process requires many T-SQL statements against many

tables. In addition, the exact statements that need to be performed and their order

are not fixed; they can (and will) vary according to which items are in stock and

which are not.

How would you write this code? You could write each of the statements

individually and execute other statements conditionally, based on the result.

You’d have to do this every time this processing was needed (and in every

application that needed it).

Alternatively, you could create a stored procedure. Stored procedures are simply

collections of one or more T-SQL statements saved for future use. You can think

of them as batch files, although in truth they are more than that.

Why Use Stored Procedures

Now that you know what stored procedures are, why use them? There are many

reasons, but here are the primary ones:

To simplify complex operations (such as the previous example) by

encapsulating processes into a single easy-to-use unit.

To ensure data integrity by not requiring that a series of steps be created

over and over. If all developers and applications use the same (tried-and-

tested) stored procedure, the same code will be used by all.

An extension of this is to prevent errors. The more steps that need to be

performed, the more likely it is that errors will be introduced. Preventing

errors ensures data consistency.

To simplify change management. If tables, column names, or business

logic (or just about anything) changes, only the stored procedure code

needs to be updated, and no one else will need even to be aware that

changes were made.

An extension of this is security. Restricting access to underlying data via

stored procedures reduces the chance of data corruption (unintentional or

otherwise).

To improve performance. Stored procedures typically execute quicker than

individual SQL statements.

Certain T-SQL language elements and SQL Server features are available

only within single requests. Stored procedures can use these to create code

that is more powerful and flexible. (You’ll see an example of this in the

next lesson.)

In other words, there are three primary benefits—simplicity, security, and

performance. Obviously all are extremely important. Before you run off to turn

all your SQL code into stored procedures, here’s the downside:

Stored procedures tend to be more complex to write than basic SQL

statements, and writing them requires a greater degree of skill and

experience.

You might not have the security access needed to create stored procedures.

Many database administrators restrict stored procedure–creation rights,

allowing users to execute them but not necessarily create them.

Nonetheless, stored procedures are very useful and should be used whenever

possible.

Note: Can’t Write Them? You Can Still Use Them

SQL Server distinguishes the security and access needed to write

stored procedures from the security and access needed to execute

them. This is a good thing; even if you can’t (or don’t want to) write

your own stored procedures, you can still execute them when

appropriate.

Using Stored Procedures

Using stored procedures requires knowing how to execute (run) them. Stored

procedures are executed far more often than they are written, so we start there.

And then we’ll look at creating and working with stored procedures.

Executing Stored Procedures

SQL Server procedures are executed using the EXECUTE statement. EXECUTE

takes the name of the stored procedure and any parameters that need to be passed

to it. Take a look at this example (you won’t be able to actually run this one until

we create it):

Input

Click here to view code image

EXECUTE productpricing @cheap OUTPUT,

@expensive OUTPUT,

@average OUTPUT

Analysis

Here, a stored procedure named productpricing is executed; it calculates

and returns the lowest, highest, and average product prices into specified

variables.

Stored procedures might or might not display results, as you will see shortly.

Tip: EXECUTE or EXEC

EXECUTE may be shortened to EXEC, but both EXECUTE and

EXEC do the exact same thing.

Creating Stored Procedures

As already explained, writing a stored procedure is not trivial. To give you a

taste for what is involved, let’s look at a simple example—a stored procedure

that returns the average product price. Here is the code:

Input

Click here to view code image

CREATE PROCEDURE productpricing AS

BEGIN

SELECT Avg(prod_price) AS priceaverage

FROM products;

END;

Analysis

The stored procedure is named productpricing and is thus defined with the

statement CREATE PROCEDURE productpricing AS. The BEGIN and

END statements are used to delimit the stored procedure body, and the body itself

is just a simple SELECT statement using the Avg() function you learned back

in Lesson 11, “Summarizing Data.”

When SQL Server processes this code, it creates a new stored procedure named

productpricing. No data is returned because the code does not call the

stored procedure; it simply creates the code for future use.

So how would you use this stored procedure? Like this:

Input

EXECUTE productpricing;

Output

priceaverage

---------------------

16.1335

Analysis

EXECUTE productpricing; executes the just-created stored procedure

and displays the returned result.

Dropping Stored Procedures

After they are created, stored procedures remain on the server, ready for use,

until dropped. The DROP command (similar to the statement you saw in Lesson

20, “Creating and Manipulating Tables”) removes the stored procedure from the

server.

To remove the stored procedure we just created, use the following statement:

Input

Click here to view code image

DROP PROCEDURE productpricing;

Analysis

This drops (deletes) the just-created stored procedure.

Working with Parameters

productpricing is a really simple stored procedure—it simply displays the

results of a SELECT statement. Typically, stored procedures do not display

results; rather, they return them to variables that you specify.

Here is an updated version of productpricing (you won’t be able to create

the stored procedure again if you did not previously drop it):

Input

Click here to view code image

CREATE PROCEDURE productpricing

@price_min MONEY OUTPUT,

@price_max MONEY OUTPUT,

@price_avg MONEY OUTPUT

BEGIN

SELECT @price_min = Min(prod_price)

FROM products;

SELECT @price_max = Max(prod_price)

FROM products;

SELECT @price_avg = Avg(prod_price)

FROM products;

END;

Analysis

This stored procedure accepts three parameters: @price_min to store the

lowest product price, @price_max to store the highest product price, and

@price_avg to store the average product price. Each parameter must have its

type specified (here, MONEY is used). The keyword OUTPUT is used to specify

that this parameter is used to send a value out of the stored procedure (back to

the caller). Without OUTPUT, the variables could only have been used to pass

values to the stored procedure. The stored procedure code itself is enclosed

within BEGIN and END statements, as shown before, and a series of SELECT

statements are performed to retrieve the values that are then saved to the

appropriate variables.

Note: Variables Must Start with @

As explained in Lesson 22, “Programming with T-SQL,” all

variable names must begin with @.

Note: Parameter Datatypes

The datatypes allowed in stored procedure parameters are the same

as those used in tables. Appendix D, “SQL Server Datatypes,” lists

these types.

To call this updated stored procedure, we must specify three variable names, as

shown here:

Input

Click here to view code image

DECLARE @cheap MONEY

DECLARE @expensive MONEY

DECLARE @average MONEY

EXECUTE productpricing @cheap OUTPUT,

@expensive OUTPUT,

@average OUTPUT

Analysis

Because the stored procedure expects three parameters, exactly three parameters

must be passed, no more and no less. Therefore, three parameters are passed to

this EXECUTE statement, and because variables are being used for the

parameters, they must first be declared using DECLARE. These three variables

are where the stored procedure returns results, so each must be passed as

OUTPUT (or values will not be returned in them). These variables need not be

named the same as the receiving variables within the stored procedure itself, and

indeed they are not in this example.

When called, this statement does not actually display any data. Rather, it returns

variables that can then be displayed (or used in other processing).

To display the retrieved average product price, you could do the following:

Input

SELECT @cheap AS Cheap;

Output

Cheap

---------------------

2.50

To obtain all three values, you can use the following:

Input

Click here to view code image

SELECT @cheap, @expensive, @average;

Output

Cheap Expensive Average

------- --------- -------

2.50 55.00 16.1335

Here is another example. This time the example passes parameters to the stored

procedure as well as returns OUTPUT parameters. ordertotal accepts an

order number and returns the total for that order:

Input

Click here to view code image

CREATE PROCEDURE ordertotal

@order_num INT,

@order_total MONEY OUTPUT

BEGIN

SELECT @order_total = Sum(item_price*quantity)

FROM orderitems

WHERE order_num = @order_num;

END;

Analysis

@order_num is used to pass a value to the stored procedure, so OUTPUT is not

needed. @order_total is defined as OUTPUT because the total is to be

returned from the stored procedure. The SELECT statement uses both of these

parameters, the WHERE clause uses @order_num to select the right rows, and

@order_total stores the calculated total.

To invoke this new stored procedure, you can use the following:

Input

Click here to view code image

DECLARE @order_total MONEY

EXECUTE ordertotal 20005, @order_total OUTPUT

SELECT @order_total

Output

---------------------

149.87

Analysis

Two parameters must be passed to ordertotal; the first is the order number

and the second is the name of the variable that will contain the calculated total.

Here, one of those parameters (the order number) is a static value (not a

variable).

To obtain a display for the total of another order, you would need to call the

stored procedure again and then redisplay the variable:

Input

Click here to view code image

DECLARE @order_total MONEY

EXECUTE ordertotal 20009, @order_total OUTPUT

SELECT @order_total

Building Intelligent Stored Procedures

All of the stored procedures used thus far have basically encapsulated simple T-

SQL SELECT statements. And although they are all valid examples of stored

procedures, they really don’t do anything more than what you could do with

those statements directly (if anything, they just make things a little more

complex). The real power of stored procedures is realized when business rules

and intelligent processing are included within them.

Consider this scenario: You need to obtain order totals as before, but also need to

add sales tax to the total, but only for some customers (perhaps the ones in your

own state). Now you need to do several things:

Obtain the total (as before).

Conditionally add tax to the total.

Return the total (with or without tax).

That’s a perfect job for a stored procedure:

Input

Click here to view code image

-- Name: ordertotal

-- Parameters: @order_num = order number

-- @taxable = 0 if not taxable, 1 if taxable

-- @order_total = order total variable

CREATE PROCEDURE ordertotal

@order_num INT,

@taxable BIT,

@order_total MONEY OUTPUT

BEGIN

-- Declare variable for total

DECLARE @total MONEY;

-- Declare tax percentage

DECLARE @taxrate INT;

-- Set tax rate (adjust as needed)

SET @taxrate = 6;

-- Get the order total

SELECT @total = Sum(item_price*quantity)

FROM orderitems

WHERE order_num = @order_num

-- Is this taxable?

IF @taxable = 1

-- Yes, so add taxrate to the total

SET @total=@total+(@total/100*@taxrate);

-- And finally, save to output variable

SELECT @order_total = @total;

END;

Tip: First DROP If Needed

You may need to DROP the existing stored procedure before you can

save this new version.

Analysis

The stored procedure has changed dramatically. First of all, comments have been

added throughout (preceded by --, as explained in Lesson 22). An additional

parameter has been added—@taxable of type BIT (which specifies 1 if

taxable, 0 if not). Within the stored procedure body, two local variables are

defined using DECLARE statements, and @taxrate in this example is set to

6%. The SELECT has changed, so the result is stored in @total (a local

variable) instead of @order_total. Then an IF statement checks to see if

@taxable is true, and if it is, a SET statement is used to add the tax to local

variable @total. And finally, @total (which might or might not have had tax

added) is saved to @order_total using another SELECT statement.

This is obviously a more sophisticated and powerful stored procedure. To try it

out, use the following two statements:

Input

Click here to view code image

DECLARE @order_total MONEY

EXECUTE ordertotal 20005, 0, @order_total OUTPUT

SELECT @order_total

Output

---------------------

149.87

Input

Click here to view code image

DECLARE @order_total MONEY

EXECUTE ordertotal 20005, 1, @order_total OUTPUT

SELECT @order_total

Output

---------------------

158.8622

Analysis

The only difference between the two EXECUTE calls is the second parameter: 0

(false) is passed for the first, and 1 (true) is passed for the second. This makes it

easy to conditionally add tax to the order total.

Summary

In this lesson, you learned what stored procedures are and why they are used.

You also learned the basics of stored procedure execution and creation syntax,

and you saw some of the ways these can be used. Stored procedures are often

used in conjunction with cursor operations, and so We look at cursors in the next

lesson.

Lesson 24. Using Cursors

In this lesson, you learn what cursors are and how to use them.

Understanding Cursors

As you have seen in previous lessons, T-SQL retrieval operations work with sets

of rows known as result sets. The rows returned are all the rows that match a

SQL statement—zero or more of them. Using simple SELECT statements, there

is no way to get the first row, the next row, or the previous 10 rows, for example.

Nor is there an easy way to process all rows, one at a time (as opposed to all of

them in a batch).

Sometimes there is a need to step through rows forward or backward, and one or

more at a time. This is what cursors are used for. A cursor is a database query

stored in SQL Server—not a SELECT statement, but the result set retrieved by

that statement. After the cursor is stored, applications can scroll or browse up

and down through the data as needed.

Cursors are used primarily by interactive applications in which users need to

scroll up and down through screens of data, browsing or making changes.

Working with Cursors

Using cursors involves several distinct steps:

1. Before a cursor can be used, it must be declared (defined). This process

does not actually retrieve any data; it merely defines the SELECT

statement to be used.

2. After it is declared, the cursor must be opened for use. This process

actually retrieves the data using the previously defined SELECT statement.

3. With the cursor populated with data, individual rows can be fetched

(retrieved) as needed.

4. When the desired data has been fetched, the cursor must be closed.

5. Finally, the cursor must be removed.

After a cursor is declared, it may be opened and closed as often as needed (until

it is removed). After the cursor is open, fetch operations can be performed as

often as needed.

Creating and Removing Cursors

Cursors are created using the DECLARE statement (discussed in Lesson 22,

“Programming with T-SQL”). DECLARE names the cursor and takes a SELECT

statement, complete with WHERE and other clauses if needed. When a cursor is

no longer needed, it must be removed using DEALLOCATE.

Note: Implicit DEALLOCATE

DEALLOCATE can actually be omitted, in which case SQL Server

will automatically remove the cursor when it goes out of scope.

For example, this statement defines a cursor named orders_cursor using a

SELECT statement that retrieves all order numbers:

Input

Click here to view code image

DECLARE orders_cursor CURSOR

FOR

SELECT order_num FROM orders ORDER BY order_num;

DEALLOCATE orders_cursor;

Analysis

A DECLARE statement is used to define and name the cursor—in this case,

orders_cursor. Nothing is done with the cursor, and it is immediately

removed using DEALLOCATE.

Note: Can Only DECLARE Once

After a cursor has been declared, it cannot be declared again, even if

the DECLARE statement is identical to the one used previously. To

change a cursor, you must first remove it with DEALLOCATE and

then declare it again with DECLARE.

Now that the cursor is defined, it is ready to be opened.

Opening and Closing Cursors

Cursors are opened using the OPEN statement, like this:

Input

OPEN orders_cursor;

Analysis

When the OPEN statement is processed, the query is executed, and the retrieved

data is stored for subsequent browsing and scrolling.

After cursor processing is complete, the cursor should be closed using the

CLOSE statement, as follows:

Input

CLOSE orders_cursor;

Analysis

CLOSE frees up internal memory and resources used by the cursor, so every

cursor should be closed when it is no longer needed.

After a cursor is closed, it cannot be reused without being opened again.

However, a cursor does not need to be declared again to be used; an OPEN

statement is sufficient (so long as the cursor has not been removed with

DEALLOCATE).

Here is an updated version of the previous example:

Input

Click here to view code image

-- Define the cursor

DECLARE orders_cursor CURSOR

FOR

SELECT order_num FROM orders ORDER BY order_num;

-- Open cursor (retrieve data)

OPEN orders_cursor;

-- Close cursor

CLOSE orders_cursor

-- And finally, remove it

DEALLOCATE orders_cursor;

Analysis

This stored procedure declares, opens, closes, and then removes a cursor.

However, nothing is done with the retrieved data.

Using Cursor Data

After a cursor is opened, you can access each row individually using a FETCH

statement. FETCH specifies the cursor to be used and where retrieved data

should be stored. It also advances the internal row pointer within the cursor so

the next FETCH statement will retrieve the next row (and not the same one over

and over).

The first example retrieves a single row from the cursor (the first row):

Input

Click here to view code image

-- Local variables

DECLARE @order_num INT;

-- Define the cursor

DECLARE orders_cursor CURSOR

FOR

SELECT order_num FROM orders ORDER BY order_num;

-- Open cursor (retrieve data)

OPEN orders_cursor;

-- Perform the first fetch (get first row)

FETCH NEXT FROM orders_cursor INTO @order_num;

-- Close cursor

CLOSE orders_cursor

-- And finally, remove it

DEALLOCATE orders_cursor;

Analysis

Here, FETCH is used to retrieve the order_num column of the current row

(it’ll start at the first row automatically) and place it into a local declared

variable named @order_num. Nothing is done with the retrieved data.

Note: What to Fetch?

The FETCH statements in this example use FETCH NEXT to fetch

the next row. This is the most frequently used FETCH, but other

FETCH options are available. These include FETCH PRIOR to

retrieve the previous row, FETCH FIRST and FETCH LAST to

retrieve the first and last rows, respectively, FETCH ABSOLUTE to

fetch a specific row number starting from the top, and FETCH

RELATIVE to fetch a specific row number starting from the current

row.

In the next example, the retrieved data is looped through from the first row to the

last:

Input

Click here to view code image

-- Local variables

DECLARE @order_num INT;

-- Define the cursor

DECLARE orders_cursor CURSOR

FOR

SELECT order_num FROM orders ORDER BY order_num;

-- Open cursor (retrieve data)

OPEN orders_cursor;

-- Perform the first fetch (get first row)

FETCH NEXT FROM orders_cursor INTO @order_num;

-- Check @@FETCH_STATUS to see if there are any more rows

-- to fetch.

WHILE @@FETCH_STATUS = 0

BEGIN

-- This is executed as long as the previous fetch succeeds.

FETCH NEXT FROM orders_cursor INTO @order_num;

END

-- Close cursor

CLOSE orders_cursor

-- And finally, remove it

DEALLOCATE orders_cursor;

Analysis

Like the previous example, this code uses FETCH to retrieve the current

order_num and place it into a declared variable named @order_num. Unlike

the previous example, the FETCH here is followed by a WHILE loop, so it is

repeated over and over. When does the looping terminate? Each time FETCH is

used, an internal function named @@FETCH_STATUS obtains a status code.

@@FETCH_STATUS will return 0 if the FETCH succeeded, and a negative value

otherwise. So the WHILE loop simply continues WHILE @@FETCH_STATUS

= 0.

With this functionality in place, you can now place any needed processing inside

the loop (after the BEGIN statement and before the next FETCH).

To put this all together, here is one further revision of our sample cursor, this

time with some actual processing of fetched data:

Input

Click here to view code image

-- Local variables

DECLARE @order_num INT;

DECLARE @order_total MONEY;

DECLARE @total MONEY;

-- Initialize @total

SET @total=0;

-- Define the cursor

DECLARE orders_cursor CURSOR

FOR

SELECT order_num FROM orders ORDER BY order_num;

-- Open cursor (retrieve data)

OPEN orders_cursor;

-- Perform the first fetch (get first row)

FETCH NEXT FROM orders_cursor INTO @order_num;

-- Check @@FETCH_STATUS to see if there are any more rows

-- to fetch.

WHILE @@FETCH_STATUS = 0

BEGIN

-- Get this order total (including tax)

EXECUTE ordertotal @order_num, 1, @order_total OUTPUT

-- Add this order to the total

SET @total = @total + @order_total

-- Get next row

FETCH NEXT FROM orders_cursor INTO @order_num;

END

-- Close cursor

CLOSE orders_cursor

-- And finally, remove it

DEALLOCATE orders_cursor;

-- And finally display calculated total

SELECT @total AS GrandTotal;

Analysis

In this example, we declared a variable named @order_total (to store the

total for each order) and another named @total (to store the running total of all

orders). FETCH fetches each @order_num as it did before, and then

EXECUTE is used to execute a stored procedure (the one we created in the

previous lesson) to calculate the total with tax for each order (the result of which

is stored in @order_total). Each time an @order_total is retrieved, it is

added to @total using a SET statement. And finally, the grand total is returned

using a SELECT.

And there you have it, a complete working example of cursors, row-by-row

processing, and even stored procedures execution.

Summary

In this lesson, you learned what cursors are and why they are used. You also saw

examples demonstrating basic cursor use, as well as techniques for looping

through cursor results and for row-by-row processing.

Lesson 25. Using Triggers

In this lesson, you learn what triggers are, why they are used, and how. You also

look at the syntax for creating and using them.

Understanding Triggers

T-SQL statements are executed when needed, as are stored procedures. But what

if you want a statement (or statements) to be executed automatically when events

occur? Here are some examples:

Every time a customer is added to a database table, check that the phone

number is formatted correctly and that the state abbreviation is in

uppercase.

Every time a product is ordered, subtract the ordered quantity from the

number in stock.

Whenever a row is deleted, save a copy in an archive table.

What all these examples have in common is that they need to be processed

automatically whenever a table change occurs. And that is exactly what triggers

are. A trigger is a T-SQL statement (or a group of statements enclosed within

BEGIN and END statements) that is automatically executed by SQL Server in

response to any of these statements:

DELETE

INSERT

UPDATE

No other T-SQL statements support triggers.

Note: Tables and Views

Triggers are supported on tables and views (but not on temporary

tables).

Creating Triggers

When creating a trigger, you need to specify three pieces of information:

The unique trigger name

The table to which the trigger is to be associated

The action that the trigger should respond to (DELETE, INSERT, or

UPDATE)

Triggers are created using the CREATE TRIGGER statement. Here is a really

simple example:

Input

Click here to view code image

CREATE TRIGGER newproduct_trigger ON products

AFTER INSERT

SELECT 'Product added';

Analysis

CREATE TRIGGER is used to create the new trigger named

newproduct_trigger. This trigger is defined as AFTER INSERT, so the

trigger will execute after a successful INSERT statement has been executed, and

the text Product added will be displayed once for each row inserted.

To test this trigger, use the INSERT statement to add one or more rows to

products; you’ll see the Product added message displayed for each

successful insertion.

Triggers are defined per event per table, and only one trigger per event per table

is allowed. As such, up to three triggers are supported per table (one for each of

INSERT, UPDATE, and DELETE).

Tip: Multiple Events Per Trigger

A single trigger can be associated with multiple events, so if you

need a trigger to be executed for both INSERT and UPDATE

operations, you can define it as AFTER INSERT, UPDATE.

Note: INSTEAD OF Triggers

Most triggers are AFTER triggers; they are executed after an event

occurs. SQL Server supports another type of trigger called an

INSTEAD OF trigger, which, if defined, is invoked instead of the

original T-SQL statement. For example, if you want to never allow

rows to be deleted, you could create an INSTEAD OF trigger that

replaces DELETE on a specific table with a T-SQL statement that

updates the rows to make them inactive (perhaps by setting a flag in

those rows). INSTEAD OF triggers are not covered in this lesson.

Dropping Triggers

By now the syntax for dropping a trigger should be self-apparent. To drop a

trigger, use the DROP TRIGGER statement, as shown here:

Input

Click here to view code image

DROP TRIGGER newproduct_trigger;

Analysis

This example removes trigger newproduct_trigger.

Tip: Updating Triggers

Triggers can be updated using ALTER TRIGGER, or they can be

dropped and re-created.

Enabling and Disabling Triggers

It is sometimes necessary to be able to execute T-SQL statements without

executing defined triggers. Rather than dropping the triggers and then having to

re-create them, SQL Server allows you to disable triggers and then enabled them

as needed.

To disable a trigger, use the DISABLE TRIGGER statement, as shown here:

Input

Click here to view code image

DISABLE TRIGGER newproduct_trigger ON products;

To reenable a trigger, use the ENABLE TRIGGER statement, as shown here:

Input

Click here to view code image

ENABLE TRIGGER newproduct_trigger ON products;

Determining Trigger Assignments

Triggers are very useful and very powerful. But they can also change the way

SQL Server behaves, executing code that you may be unaware of. And because

most triggers execute silently (they provide no feedback), it can be difficult to

determine whether triggers are running at any given time.

To solve this problem, you can use the built-in stored procedure

SP_HELPTRIGGER:

Input

SP_HELPTRIGGER products;

Analysis

SP_HELPTRIGGER takes the name of a table and returns a list of triggers (if

any are defined), with flags indicating the trigger type. If you were to run this

code after creating the newproduct trigger discussed above, ISINSERT and

ISAFTER would both be 1 because we created that trigger AFTER INSERT.

Using Triggers

With the basics covered, we now look at each of the supported trigger types and

the differences between them.

INSERT Triggers

INSERT triggers are executed after an INSERT statement is executed. Within

INSERT trigger code, you can refer to a virtual table named INSERTED to

access the rows being inserted.

Here’s an example (a really useful one, actually). IDENTITY columns have

values that are automatically assigned by SQL Server. Lesson 20, “Creating and

Manipulating Tables,” suggested several ways to determine the newly generated

value, but here is an even better solution:

Input

Click here to view code image

CREATE TRIGGER neworder_trigger ON orders

AFTER INSERT

SELECT @@IDENTITY AS order_num;

Analysis

The code creates a trigger named neworder_trigger that is executed

AFTER INSERT on the table orders. When a new order is saved in orders,

SQL Server generates a new order number and saves it in order_num. This

trigger simply obtains this value from @@IDENTITY and returns it. Using this

trigger for every insertion into orders will always return the new order

number.

To test this trigger, try inserting a new order, like this:

Input

Click here to view code image

INSERT INTO orders(order_date, cust_id)

VALUES(GetDate(), 10001);

Output

order_num

------------

20010

Analysis

orders contains three columns. order_date and cust_id must be

specified, order_num is automatically generated by SQL Server, and

order_num is now returned automatically.

DELETE Triggers

DELETE triggers are executed after a DELETE statement is executed. Within

DELETE trigger code, you can refer to a virtual table named DELETED to access

the rows being deleted.

The following example demonstrates the use of DELETED to save deleted rows

into an archive table:

Input

Click here to view code image

CREATE TRIGGER deleteorder_trigger ON orders

AFTER DELETE

BEGIN

INSERT INTO orders_archive(order_num, order_date, cust_id)

SELECT order_num, order_date, cust_id FROM DELETED;

END;

Analysis

This trigger is executed when rows are deleted from the orders table. It uses

an INSERT SELECT statement to save the rows in DELETE into an archive

table named orders_archive. (To actually use this example, you need to

create a table named orders_archive with the same columns as orders.)

Note: Multistatement Triggers

You’ll notice that trigger deleteorder_trigger uses BEGIN

and END statements to mark the trigger body. This is actually not

necessary in this example, although it does no harm being there.

The advantage of using a BEGIN END block is that the trigger

would then be able to accommodate multiple SQL statements (one

after the other within the BEGIN END block). BEGIN END was

introduced in Lesson 22.

UPDATE Triggers

UPDATE triggers are executed after an UPDATE statement is executed. Within

UPDATE trigger code, you can refer to a virtual table named DELETED to access

the previous (pre-UPDATE statement) values and INSERTED to access the new

updated values.

The following example ensures that state abbreviations are always in uppercase

(regardless of how they were actually specified in the UPDATE statement):

Input

Click here to view code image

CREATE TRIGGER vendor_trigger ON vendors

AFTER INSERT, UPDATE

BEGIN

UPDATE vendors

SET vend_state=Upper(vend_state)

WHERE vend_id IN (SELECT vend_id FROM INSERTED);

END;

Analysis

This trigger is executed AFTER INSERT, UPDATE. Each time rows are

inserted or updated, the values in vend_state are replaced with

Upper(vend_state).